diff --git a/.github/scripts/sync_extras.py b/.github/scripts/sync_extras.py index 3755cee2..ec8065e0 100755 --- a/.github/scripts/sync_extras.py +++ b/.github/scripts/sync_extras.py @@ -37,6 +37,7 @@ "rag": ("RAG proxy (ChromaDB, embeddings)", ["chromadb", "pydantic_ai"]), "memory": ("Long-term memory proxy", ["chromadb", "yaml", "pydantic_ai"]), "vad": ("Voice Activity Detection (Silero VAD via ONNX)", ["onnxruntime"]), + "diarization": ("Speaker diarization (pyannote.audio)", ["pyannote.audio"]), "whisper": ("Local Whisper ASR (faster-whisper)", ["faster_whisper"]), "whisper-mlx": ("MLX Whisper for Apple Silicon", ["mlx_whisper"]), "tts": ("Local Piper TTS", ["piper"]), diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index d614623e..b184f0a7 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -33,7 +33,7 @@ jobs: run: uv run --all-extras pytest -vvv - name: Run pytest (non-macOS - exclude mlx-whisper) if: matrix.os != 'macos-latest' - run: uv run --extra audio --extra llm --extra rag --extra memory --extra vad --extra faster-whisper --extra piper --extra kokoro --extra server --extra speed --extra test pytest -vvv + run: uv run --extra audio --extra diarization --extra llm --extra rag --extra memory --extra vad --extra faster-whisper --extra piper --extra kokoro --extra server --extra speed --extra test pytest -vvv - name: Upload coverage reports to Codecov if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.13' uses: codecov/codecov-action@v5 diff --git a/README.md b/README.md index bd6172aa..bf2887bd 100644 --- a/README.md +++ b/README.md @@ -421,6 +421,7 @@ agent-cli install-extras rag memory vad Available extras: • audio - Audio recording/playback + • diarization - Speaker diarization (pyannote.audio) • faster-whisper - Whisper ASR via CTranslate2 • kokoro - Kokoro neural TTS (GPU) • llm - LLM framework (pydantic-ai) @@ -444,9 +445,9 @@ agent-cli install-extras rag memory vad ╭─ Arguments ────────────────────────────────────────────────────────────────────────────╮ -│ extras [EXTRAS]... Extras to install: audio, faster-whisper, kokoro, llm, │ -│ memory, mlx-whisper, piper, rag, server, speed, vad, │ -│ whisper-transformers, wyoming │ +│ extras [EXTRAS]... Extras to install: audio, diarization, faster-whisper, │ +│ kokoro, llm, memory, mlx-whisper, piper, rag, server, │ +│ speed, vad, whisper-transformers, wyoming │ ╰────────────────────────────────────────────────────────────────────────────────────────╯ ╭─ Options ──────────────────────────────────────────────────────────────────────────────╮ │ --list -l Show available extras with descriptions (what each one enables) │ @@ -730,7 +731,7 @@ the `[defaults]` section of your configuration file. │ --llm --no-llm Clean up transcript with LLM: fix errors, │ │ add punctuation, remove filler words. Uses │ │ --extra-instructions if set (via CLI or │ -│ config file). │ +│ config file). Not compatible with --diarize. │ │ [default: no-llm] │ ╰────────────────────────────────────────────────────────────────────────────────────────╯ ╭─ Audio Recovery ───────────────────────────────────────────────────────────────────────╮ @@ -852,6 +853,44 @@ the `[defaults]` section of your configuration file. │ provide context for │ │ LLM cleanup. │ ╰────────────────────────────────────────────────────────────────────────────────────────╯ +╭─ Diarization ──────────────────────────────────────────────────────────────────────────╮ +│ --diarize --no-diarize Enable speaker diarization │ +│ (requires pyannote-audio). │ +│ Install with: pip install │ +│ agent-cli[diarization] │ +│ [default: no-diarize] │ +│ --diarize-format [inline|json] Output format for diarization │ +│ ('inline' for [Speaker N]: │ +│ text, 'json' for structured │ +│ output). │ +│ [default: inline] │ +│ --hf-token TEXT HuggingFace token for pyannote │ +│ models. Required for │ +│ diarization. Token must have │ +│ 'Read access to contents of all │ +│ public gated repos you can │ +│ access' permission. Accept │ +│ licenses at: │ +│ https://hf.co/pyannote/speaker… │ +│ https://hf.co/pyannote/segment… │ +│ https://hf.co/pyannote/wespeak… │ +│ [env var: HF_TOKEN] │ +│ --min-speakers INTEGER Minimum number of speakers │ +│ (optional hint for │ +│ diarization). │ +│ --max-speakers INTEGER Maximum number of speakers │ +│ (optional hint for │ +│ diarization). │ +│ --align-words --no-align-words Use wav2vec2 forced alignment │ +│ for word-level speaker │ +│ assignment (more accurate but │ +│ slower). │ +│ [default: no-align-words] │ +│ --align-language TEXT Language code for word │ +│ alignment model (e.g., 'en', │ +│ 'fr', 'de', 'es', 'it'). │ +│ [default: en] │ +╰────────────────────────────────────────────────────────────────────────────────────────╯ ``` @@ -1050,7 +1089,7 @@ uv tool install "agent-cli[vad]" -p 3.13 ╭─ LLM Configuration ────────────────────────────────────────────────────────────────────╮ │ --llm --no-llm Clean up transcript with LLM: fix errors, add punctuation, │ │ remove filler words. Uses --extra-instructions if set (via CLI │ -│ or config file). │ +│ or config file). Not compatible with --diarize. │ │ [default: no-llm] │ ╰────────────────────────────────────────────────────────────────────────────────────────╯ ╭─ Process Management ───────────────────────────────────────────────────────────────────╮ diff --git a/agent_cli/_extras.json b/agent_cli/_extras.json index 1e27ed17..3d0b266a 100644 --- a/agent_cli/_extras.json +++ b/agent_cli/_extras.json @@ -5,6 +5,12 @@ "sounddevice" ] ], + "diarization": [ + "Speaker diarization (pyannote.audio)", + [ + "pyannote.audio" + ] + ], "faster-whisper": [ "Whisper ASR via CTranslate2", [ diff --git a/agent_cli/_requirements/diarization.txt b/agent_cli/_requirements/diarization.txt new file mode 100644 index 00000000..8af13109 --- /dev/null +++ b/agent_cli/_requirements/diarization.txt @@ -0,0 +1,405 @@ +# This file was autogenerated by uv via the following command: +# uv export --extra diarization --no-dev --no-emit-project --no-hashes +aiohappyeyeballs==2.6.1 + # via aiohttp +aiohttp==3.13.3 + # via fsspec +aiosignal==1.4.0 + # via aiohttp +alembic==1.18.3 + # via optuna +annotated-types==0.7.0 + # via pydantic +anyio==4.12.1 + # via httpx +asteroid-filterbanks==0.4.0 + # via pyannote-audio +attrs==25.4.0 + # via aiohttp +certifi==2026.1.4 + # via + # httpcore + # httpx + # requests +cffi==2.0.0 + # via soundfile +charset-normalizer==3.4.4 + # via requests +click==8.3.1 + # via + # typer + # typer-slim +colorama==0.4.6 ; sys_platform == 'win32' + # via + # click + # colorlog + # tqdm +colorlog==6.10.1 + # via optuna +contourpy==1.3.3 + # via matplotlib +cycler==0.12.1 + # via matplotlib +dotenv==0.9.9 + # via agent-cli +einops==0.8.2 + # via pyannote-audio +filelock==3.20.3 + # via + # huggingface-hub + # pyannote-pipeline + # torch +fonttools==4.61.1 + # via matplotlib +frozenlist==1.8.0 + # via + # aiohttp + # aiosignal +fsspec==2026.1.0 + # via + # huggingface-hub + # lightning + # pytorch-lightning + # torch +googleapis-common-protos==1.72.0 + # via + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http +greenlet==3.3.1 ; platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64' + # via sqlalchemy +grpcio==1.76.0 + # via opentelemetry-exporter-otlp-proto-grpc +h11==0.16.0 + # via httpcore +hf-xet==1.2.0 ; platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' + # via huggingface-hub +httpcore==1.0.9 + # via httpx +httpx==0.28.1 + # via agent-cli +huggingface-hub==0.36.0 + # via pyannote-audio +idna==3.11 + # via + # anyio + # httpx + # requests + # yarl +importlib-metadata==8.7.1 + # via opentelemetry-api +jinja2==3.1.6 + # via torch +joblib==1.5.3 + # via scikit-learn +julius==0.2.7 + # via torch-audiomentations +kiwisolver==1.4.9 + # via matplotlib +lightning==2.6.1 + # via pyannote-audio +lightning-utilities==0.15.2 + # via + # lightning + # pytorch-lightning + # torchmetrics +mako==1.3.10 + # via alembic +markdown-it-py==4.0.0 + # via rich +markupsafe==3.0.3 + # via + # jinja2 + # mako +matplotlib==3.10.8 + # via pyannote-audio +mdurl==0.1.2 + # via markdown-it-py +mpmath==1.3.0 + # via sympy +multidict==6.7.1 + # via + # aiohttp + # yarl +networkx==3.6.1 + # via torch +numpy==2.3.5 + # via + # asteroid-filterbanks + # contourpy + # matplotlib + # optuna + # pandas + # pyannote-core + # pyannote-metrics + # pytorch-metric-learning + # scikit-learn + # scipy + # soundfile + # torchmetrics +nvidia-cublas-cu12==12.8.4.1 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via + # nvidia-cudnn-cu12 + # nvidia-cusolver-cu12 + # torch +nvidia-cuda-cupti-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +nvidia-cuda-nvrtc-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +nvidia-cuda-runtime-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +nvidia-cudnn-cu12==9.10.2.21 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +nvidia-cufft-cu12==11.3.3.83 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +nvidia-cufile-cu12==1.13.1.3 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +nvidia-curand-cu12==10.3.9.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +nvidia-cusolver-cu12==11.7.3.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +nvidia-cusparse-cu12==12.5.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via + # nvidia-cusolver-cu12 + # torch +nvidia-cusparselt-cu12==0.7.1 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +nvidia-nccl-cu12==2.27.3 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +nvidia-nvjitlink-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via + # nvidia-cufft-cu12 + # nvidia-cusolver-cu12 + # nvidia-cusparse-cu12 + # torch +nvidia-nvtx-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +opentelemetry-api==1.39.1 + # via + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http + # opentelemetry-sdk + # opentelemetry-semantic-conventions + # pyannote-audio +opentelemetry-exporter-otlp==1.39.1 + # via pyannote-audio +opentelemetry-exporter-otlp-proto-common==1.39.1 + # via + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http +opentelemetry-exporter-otlp-proto-grpc==1.39.1 + # via opentelemetry-exporter-otlp +opentelemetry-exporter-otlp-proto-http==1.39.1 + # via opentelemetry-exporter-otlp +opentelemetry-proto==1.39.1 + # via + # opentelemetry-exporter-otlp-proto-common + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http +opentelemetry-sdk==1.39.1 + # via + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http + # pyannote-audio +opentelemetry-semantic-conventions==0.60b1 + # via opentelemetry-sdk +optuna==4.7.0 + # via pyannote-pipeline +packaging==25.0 + # via + # huggingface-hub + # lightning + # lightning-utilities + # matplotlib + # optuna + # pytorch-lightning + # torch-pitch-shift + # torchmetrics +pandas==3.0.0 + # via + # pyannote-core + # pyannote-database + # pyannote-metrics +pillow==12.1.0 + # via matplotlib +primepy==1.3 + # via torch-pitch-shift +propcache==0.4.1 + # via + # aiohttp + # yarl +protobuf==6.33.4 + # via + # googleapis-common-protos + # opentelemetry-proto +psutil==7.2.1 ; sys_platform == 'win32' + # via agent-cli +pyannote-audio==4.0.3 + # via agent-cli +pyannote-core==6.0.1 + # via + # pyannote-audio + # pyannote-database + # pyannote-metrics + # pyannote-pipeline +pyannote-database==6.1.1 + # via + # pyannote-audio + # pyannote-metrics + # pyannote-pipeline +pyannote-metrics==4.0.0 + # via pyannote-audio +pyannote-pipeline==4.0.0 + # via pyannote-audio +pyannoteai-sdk==0.4.0 + # via pyannote-audio +pycparser==2.23 ; implementation_name != 'PyPy' + # via cffi +pydantic==2.12.5 + # via agent-cli +pydantic-core==2.41.5 + # via pydantic +pygments==2.19.2 + # via rich +pyparsing==3.3.1 + # via matplotlib +pyperclip==1.11.0 + # via agent-cli +python-dateutil==2.9.0.post0 + # via + # matplotlib + # pandas +python-dotenv==1.2.1 + # via dotenv +pytorch-lightning==2.6.1 + # via lightning +pytorch-metric-learning==2.9.0 + # via pyannote-audio +pyyaml==6.0.3 + # via + # huggingface-hub + # lightning + # optuna + # pyannote-database + # pyannote-pipeline + # pytorch-lightning +requests==2.32.5 + # via + # huggingface-hub + # opentelemetry-exporter-otlp-proto-http + # pyannoteai-sdk +rich==14.2.0 + # via + # agent-cli + # pyannote-audio + # typer + # typer-slim +safetensors==0.7.0 + # via pyannote-audio +scikit-learn==1.8.0 + # via + # pyannote-metrics + # pytorch-metric-learning +scipy==1.17.0 + # via + # pyannote-metrics + # scikit-learn +setproctitle==1.3.7 + # via agent-cli +setuptools==80.9.0 + # via + # lightning-utilities + # torch + # triton +shellingham==1.5.4 + # via + # typer + # typer-slim +six==1.17.0 + # via python-dateutil +sortedcontainers==2.4.0 + # via pyannote-core +soundfile==0.13.1 + # via pyannote-audio +sqlalchemy==2.0.46 + # via + # alembic + # optuna +sympy==1.14.0 + # via torch +threadpoolctl==3.6.0 + # via scikit-learn +torch==2.8.0 + # via + # asteroid-filterbanks + # julius + # lightning + # pyannote-audio + # pytorch-lightning + # pytorch-metric-learning + # torch-audiomentations + # torch-pitch-shift + # torchaudio + # torchmetrics +torch-audiomentations==0.12.0 + # via pyannote-audio +torch-pitch-shift==1.2.5 + # via torch-audiomentations +torchaudio==2.8.0 + # via + # pyannote-audio + # torch-audiomentations + # torch-pitch-shift +torchcodec==0.7.0 + # via pyannote-audio +torchmetrics==1.8.2 + # via + # lightning + # pyannote-audio + # pytorch-lightning +tqdm==4.67.1 + # via + # huggingface-hub + # lightning + # optuna + # pyannote-pipeline + # pytorch-lightning + # pytorch-metric-learning +triton==3.4.0 ; platform_machine == 'x86_64' and sys_platform == 'linux' + # via torch +typer==0.21.1 + # via agent-cli +typer-slim==0.21.1 + # via agent-cli +typing-extensions==4.15.0 + # via + # aiosignal + # alembic + # anyio + # asteroid-filterbanks + # grpcio + # huggingface-hub + # lightning + # lightning-utilities + # opentelemetry-api + # opentelemetry-exporter-otlp-proto-grpc + # opentelemetry-exporter-otlp-proto-http + # opentelemetry-sdk + # opentelemetry-semantic-conventions + # pydantic + # pydantic-core + # pytorch-lightning + # sqlalchemy + # torch + # typer + # typer-slim + # typing-inspection +typing-inspection==0.4.2 + # via pydantic +tzdata==2025.3 ; sys_platform == 'emscripten' or sys_platform == 'win32' + # via pandas +urllib3==2.3.0 + # via requests +yarl==1.22.0 + # via aiohttp +zipp==3.23.0 + # via importlib-metadata diff --git a/agent_cli/_requirements/kokoro.txt b/agent_cli/_requirements/kokoro.txt index 4857fd38..05ae9f60 100644 --- a/agent_cli/_requirements/kokoro.txt +++ b/agent_cli/_requirements/kokoro.txt @@ -197,7 +197,7 @@ nvidia-cusparse-cu12==12.5.8.93 ; platform_machine == 'x86_64' and sys_platform # torch nvidia-cusparselt-cu12==0.7.1 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch -nvidia-nccl-cu12==2.27.5 ; platform_machine == 'x86_64' and sys_platform == 'linux' +nvidia-nccl-cu12==2.27.3 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch nvidia-nvjitlink-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via @@ -205,8 +205,6 @@ nvidia-nvjitlink-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform = # nvidia-cusolver-cu12 # nvidia-cusparse-cu12 # torch -nvidia-nvshmem-cu12==3.3.20 ; platform_machine == 'x86_64' and sys_platform == 'linux' - # via torch nvidia-nvtx-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch packaging==25.0 @@ -315,6 +313,7 @@ setuptools==80.9.0 # spacy # thinc # torch + # triton shellingham==1.5.4 # via # typer @@ -349,7 +348,7 @@ thinc==8.3.10 # via spacy tokenizers==0.22.2 # via transformers -torch==2.9.1 +torch==2.8.0 # via # curated-transformers # kokoro @@ -363,7 +362,7 @@ transformers==4.57.5 # via # agent-cli # kokoro -triton==3.5.1 ; platform_machine == 'x86_64' and sys_platform == 'linux' +triton==3.4.0 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch typer==0.21.1 # via diff --git a/agent_cli/_requirements/mlx-whisper.txt b/agent_cli/_requirements/mlx-whisper.txt index 3b8de140..ac906078 100644 --- a/agent_cli/_requirements/mlx-whisper.txt +++ b/agent_cli/_requirements/mlx-whisper.txt @@ -163,7 +163,9 @@ sentry-sdk==2.49.0 ; platform_machine == 'arm64' and sys_platform == 'darwin' setproctitle==1.3.7 # via agent-cli setuptools==80.9.0 ; python_full_version >= '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin' - # via torch + # via + # torch + # triton shellingham==1.5.4 # via # typer @@ -174,7 +176,7 @@ sympy==1.14.0 ; platform_machine == 'arm64' and sys_platform == 'darwin' # via torch tiktoken==0.12.0 ; platform_machine == 'arm64' and sys_platform == 'darwin' # via mlx-whisper -torch==2.9.1 ; platform_machine == 'arm64' and sys_platform == 'darwin' +torch==2.8.0 ; platform_machine == 'arm64' and sys_platform == 'darwin' # via mlx-whisper tqdm==4.67.1 ; platform_machine == 'arm64' and sys_platform == 'darwin' # via diff --git a/agent_cli/_requirements/whisper-transformers.txt b/agent_cli/_requirements/whisper-transformers.txt index a9d0037c..bd4cdb68 100644 --- a/agent_cli/_requirements/whisper-transformers.txt +++ b/agent_cli/_requirements/whisper-transformers.txt @@ -121,7 +121,7 @@ nvidia-cusparse-cu12==12.5.8.93 ; platform_machine == 'x86_64' and sys_platform # torch nvidia-cusparselt-cu12==0.7.1 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch -nvidia-nccl-cu12==2.27.5 ; platform_machine == 'x86_64' and sys_platform == 'linux' +nvidia-nccl-cu12==2.27.3 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch nvidia-nvjitlink-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via @@ -129,8 +129,6 @@ nvidia-nvjitlink-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform = # nvidia-cusolver-cu12 # nvidia-cusparse-cu12 # torch -nvidia-nvshmem-cu12==3.3.20 ; platform_machine == 'x86_64' and sys_platform == 'linux' - # via torch nvidia-nvtx-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch packaging==25.0 @@ -192,8 +190,10 @@ sentry-sdk==2.49.0 # via fastapi-cloud-cli setproctitle==1.3.7 # via agent-cli -setuptools==80.9.0 ; python_full_version >= '3.12' - # via torch +setuptools==80.9.0 ; (python_full_version >= '3.12' and platform_machine != 'x86_64') or (python_full_version >= '3.12' and sys_platform != 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') + # via + # torch + # triton shellingham==1.5.4 # via # typer @@ -204,7 +204,7 @@ sympy==1.14.0 # via torch tokenizers==0.22.2 # via transformers -torch==2.9.1 +torch==2.8.0 # via agent-cli tqdm==4.67.1 # via @@ -212,7 +212,7 @@ tqdm==4.67.1 # transformers transformers==4.57.5 # via agent-cli -triton==3.5.1 ; platform_machine == 'x86_64' and sys_platform == 'linux' +triton==3.4.0 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch typer==0.21.1 # via diff --git a/agent_cli/agents/transcribe.py b/agent_cli/agents/transcribe.py index bce561eb..7bf4ff4d 100644 --- a/agent_cli/agents/transcribe.py +++ b/agent_cli/agents/transcribe.py @@ -19,6 +19,12 @@ from agent_cli.core import process from agent_cli.core.audio import setup_devices from agent_cli.core.deps import requires_extras +from agent_cli.core.diarization import ( + SpeakerDiarizer, + align_transcript_with_speakers, + align_transcript_with_words, + format_diarized_output, +) from agent_cli.core.utils import ( enable_json_mode, format_short_timedelta, @@ -248,6 +254,53 @@ def log_transcription( f.write(json.dumps(log_entry) + "\n") +def _apply_diarization( + transcript: str, + audio_path: Path, + diarization_cfg: config.Diarization, + quiet: bool, +) -> str: + """Apply speaker diarization to transcript.""" + if not quiet: + print_with_style("🎙️ Running speaker diarization...", style="blue") + + assert diarization_cfg.hf_token is not None + diarizer = SpeakerDiarizer( + hf_token=diarization_cfg.hf_token, + min_speakers=diarization_cfg.min_speakers, + max_speakers=diarization_cfg.max_speakers, + ) + segments = diarizer.diarize(audio_path) + + if not segments: + LOGGER.warning("Diarization returned no segments") + return transcript + + # Align transcript with speaker segments + if diarization_cfg.align_words: + if not quiet: + print_with_style("🔤 Running word-level alignment...", style="blue") + segments = align_transcript_with_words( + transcript, + segments, + audio_path=audio_path, + language=diarization_cfg.align_language, + ) + else: + segments = align_transcript_with_speakers(transcript, segments) + + # Format output + result = format_diarized_output( + segments, + output_format=diarization_cfg.diarize_format, + ) + if not quiet: + num_speakers = len({s.speaker for s in segments}) + print_with_style(f"✅ Identified {num_speakers} speaker(s)", style="green") + + return result + + async def _async_main( # noqa: PLR0912, PLR0915, C901 *, extra_instructions: str | None, @@ -266,6 +319,7 @@ async def _async_main( # noqa: PLR0912, PLR0915, C901 audio_file_path: Path | None = None, save_recording: bool = True, process_name: str | None = None, + diarization_cfg: config.Diarization | None = None, ) -> TranscriptResult: """Unified async entry point for both live and file-based transcription.""" start_time = time.monotonic() @@ -354,6 +408,42 @@ async def _async_main( # noqa: PLR0912, PLR0915, C901 elapsed = time.monotonic() - start_time + # Apply diarization if enabled + if diarization_cfg and diarization_cfg.diarize and transcript: + # Determine audio file path for diarization + diarize_audio_path = audio_file_path + if not diarize_audio_path and save_recording: + diarize_audio_path = get_last_recording(1) + + if diarize_audio_path and diarize_audio_path.exists(): + try: + transcript = _apply_diarization( + transcript, + diarize_audio_path, + diarization_cfg, + quiet=general_cfg.quiet, + ) + except ImportError as e: + print_with_style(f"❌ Diarization failed: {e}", style="red") + except Exception as e: + LOGGER.exception("Diarization failed") + error_msg = str(e) + if "403" in error_msg or "gated" in error_msg.lower(): + print_with_style( + "❌ Diarization failed: HuggingFace model access denied.\n" + "Accept licenses for ALL required models:\n" + " • https://hf.co/pyannote/speaker-diarization-3.1\n" + " • https://hf.co/pyannote/segmentation-3.0\n" + " • https://hf.co/pyannote/wespeaker-voxceleb-resnet34-LM\n" + " • https://hf.co/pyannote/speaker-diarization-community-1\n" + "Token must have 'Read access to public gated repos' permission.", + style="red", + ) + else: + print_with_style(f"❌ Diarization error: {e}", style="red") + else: + LOGGER.warning("No audio file available for diarization") + if llm_enabled and transcript: if not general_cfg.quiet: print_input_panel( @@ -466,7 +556,7 @@ async def _async_main( # noqa: PLR0912, PLR0915, C901 @app.command("transcribe", rich_help_panel="Voice Commands") @requires_extras("audio", "llm") -def transcribe( # noqa: PLR0912 +def transcribe( # noqa: PLR0912, PLR0911, PLR0915, C901 *, extra_instructions: str | None = typer.Option( None, @@ -512,6 +602,14 @@ def transcribe( # noqa: PLR0912 config_file: str | None = opts.CONFIG_FILE, print_args: bool = opts.PRINT_ARGS, transcription_log: Path | None = opts.TRANSCRIPTION_LOG, + # --- Diarization Options --- + diarize: bool = opts.DIARIZE, + diarize_format: opts.DiarizeFormat = opts.DIARIZE_FORMAT, + hf_token: str | None = opts.HF_TOKEN, + min_speakers: int | None = opts.MIN_SPEAKERS, + max_speakers: int | None = opts.MAX_SPEAKERS, + align_words: bool = opts.ALIGN_WORDS, + align_language: str = opts.ALIGN_LANGUAGE, ) -> None: """Record audio from microphone and transcribe to text. @@ -546,6 +644,42 @@ def transcribe( # noqa: PLR0912 if transcription_log: transcription_log = transcription_log.expanduser() + # Validate diarization options + if diarize: + if llm: + print_with_style( + "❌ --llm cannot be used with --diarize. Speaker labels must remain unchanged.", + style="red", + ) + return + if not hf_token: + print_with_style( + "❌ --hf-token required for diarization. " + "Set HF_TOKEN env var or pass --hf-token. " + "Token must have 'Read access to contents of all public gated repos you can access' permission. " + "Accept licenses at: https://hf.co/pyannote/speaker-diarization-3.1, " + "https://hf.co/pyannote/segmentation-3.0, https://hf.co/pyannote/wespeaker-voxceleb-resnet34-LM", + style="red", + ) + return + if not save_recording and not from_file and last_recording == 0: + print_with_style( + "❌ Diarization requires audio file. Use --save-recording (default) " + "or --from-file/--last-recording.", + style="red", + ) + return + + diarization_cfg = config.Diarization( + diarize=diarize, + diarize_format=diarize_format, + hf_token=hf_token, + min_speakers=min_speakers, + max_speakers=max_speakers, + align_words=align_words, + align_language=align_language, + ) + # Handle recovery options if last_recording and from_file: print_with_style("❌ Cannot use both --last-recording and --from-file", style="red") @@ -635,6 +769,7 @@ def transcribe( # noqa: PLR0912 gemini_llm_cfg=gemini_llm_cfg, llm_enabled=llm, transcription_log=transcription_log, + diarization_cfg=diarization_cfg, ), ) if json_output: @@ -683,6 +818,7 @@ def transcribe( # noqa: PLR0912 transcription_log=transcription_log, save_recording=save_recording, process_name=process_name, + diarization_cfg=diarization_cfg, ), ) if json_output: diff --git a/agent_cli/config.py b/agent_cli/config.py index 636de280..bc8a6025 100644 --- a/agent_cli/config.py +++ b/agent_cli/config.py @@ -256,6 +256,21 @@ def _expand_user_path(cls, v: str | None) -> Path | None: return None +# --- Panel: Diarization Options --- + + +class Diarization(BaseModel): + """Configuration for speaker diarization.""" + + diarize: bool = False + diarize_format: Literal["inline", "json"] = "inline" + hf_token: str | None = None + min_speakers: int | None = None + max_speakers: int | None = None + align_words: bool = False + align_language: str = "en" + + # --- Panel: Dev (Parallel Development) Options --- diff --git a/agent_cli/core/alignment.py b/agent_cli/core/alignment.py new file mode 100644 index 00000000..35d31af5 --- /dev/null +++ b/agent_cli/core/alignment.py @@ -0,0 +1,416 @@ +"""Forced alignment using wav2vec2 for word-level timestamps. + +Based on whisperx's alignment approach with beam search backtracking. +""" + +from __future__ import annotations + +import math +from dataclasses import dataclass +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from pathlib import Path + + import torch + +SAMPLE_RATE = 16000 +# WhisperX's backtrack_beam signature defaults to 5, but align() calls it with 2. +DEFAULT_BEAM_WIDTH = 2 +MIN_WAV2VEC2_SAMPLES = 400 + +# Torchaudio bundled models +ALIGN_MODELS: dict[str, str] = { + "en": "WAV2VEC2_ASR_BASE_960H", + "fr": "VOXPOPULI_ASR_BASE_10K_FR", + "de": "VOXPOPULI_ASR_BASE_10K_DE", + "es": "VOXPOPULI_ASR_BASE_10K_ES", + "it": "VOXPOPULI_ASR_BASE_10K_IT", +} + + +@dataclass +class AlignedWord: + """A word with start/end timestamps.""" + + word: str + start: float + end: float + + +def align( + audio_path: Path, + transcript: str, + language: str = "en", + device: str = "cpu", +) -> list[AlignedWord]: + """Align transcript to audio, returning word-level timestamps. + + Args: + audio_path: Path to audio file. + transcript: Text to align. + language: Language code (en, fr, de, es, it). + device: Device to run on (cpu or cuda). + + Returns: + List of words with timestamps. + + """ + import torch # noqa: PLC0415 + import torchaudio # noqa: PLC0415 + + if language not in ALIGN_MODELS: + msg = f"No alignment model for language: {language}. Supported: {list(ALIGN_MODELS.keys())}" + raise ValueError(msg) + + # Load model + bundle = torchaudio.pipelines.__dict__[ALIGN_MODELS[language]] + model = bundle.get_model().to(device) + labels = bundle.get_labels() + dictionary = {c.lower(): i for i, c in enumerate(labels)} + + # Load audio + waveform, sample_rate = torchaudio.load(str(audio_path)) + if sample_rate != SAMPLE_RATE: + waveform = torchaudio.functional.resample(waveform, sample_rate, SAMPLE_RATE) + sample_rate = SAMPLE_RATE + + # Handle minimum input length for wav2vec2 models + # Save original length before padding for correct duration computation + original_length = waveform.shape[-1] + lengths = None + if waveform.shape[-1] < MIN_WAV2VEC2_SAMPLES: + lengths = torch.as_tensor([waveform.shape[-1]]).to(device) + waveform = torch.nn.functional.pad( + waveform, + (0, MIN_WAV2VEC2_SAMPLES - waveform.shape[-1]), + ) + + # Get emissions + with torch.inference_mode(): + emissions, _ = model(waveform.to(device), lengths=lengths) + emissions = torch.log_softmax(emissions, dim=-1).cpu() + + emission = emissions[0] + words = transcript.split() + if not words: + return [] + tokens, token_to_word = _build_alignment_tokens(words, dictionary) + if not tokens: + return _fallback_word_alignment(words, waveform, sample_rate) + + # CTC forced alignment + blank_id = _get_blank_id(dictionary) + trellis = _get_trellis(emission, tokens, blank_id) + path = _backtrack(trellis, emission, tokens, blank_id) + if not path: + return _fallback_word_alignment(words, waveform, sample_rate) + char_segments = _merge_repeats(path) + + # Convert to words + if trellis.shape[0] <= 1: + return _fallback_word_alignment(words, waveform, sample_rate) + + duration = original_length / sample_rate + ratio = duration / (trellis.shape[0] - 1) + + return _segments_to_words(char_segments, token_to_word, words, ratio) + + +def _get_blank_id(dictionary: dict[str, int]) -> int: + for char, code in dictionary.items(): + if char in ("[pad]", ""): + return code + return 0 + + +def _build_alignment_tokens( + words: list[str], + dictionary: dict[str, int], +) -> tuple[list[int], list[int | None]]: + """Build token sequence for alignment with wildcard support. + + Characters not in dictionary get token -1 (wildcard). + This allows alignment to proceed even with unknown characters. + """ + tokens: list[int] = [] + token_to_word: list[int | None] = [] + word_separator = dictionary.get("|") + + for index, word in enumerate(words): + for char in word: + char_lower = char.lower() + if char_lower in dictionary: + tokens.append(dictionary[char_lower]) + else: + # Use wildcard (-1) for unknown characters + tokens.append(-1) + token_to_word.append(index) + if word_separator is not None and index < len(words) - 1: + tokens.append(word_separator) + token_to_word.append(None) + + return tokens, token_to_word + + +def _get_wildcard_emission( + frame_emission: torch.Tensor, + tokens: list[int], + blank_id: int, +) -> torch.Tensor: + """Get emission scores, using max non-blank for wildcard tokens (-1). + + Wildcards are used for characters not in the model's dictionary. + For these, we use the maximum probability across all non-blank tokens. + """ + import torch # noqa: PLC0415 + + tokens_tensor = torch.tensor(tokens) if not isinstance(tokens, torch.Tensor) else tokens + wildcard_mask = tokens_tensor == -1 + + # Get scores for non-wildcard positions (clamp to avoid -1 index) + regular_scores = frame_emission[tokens_tensor.clamp(min=0).long()] + + # For wildcards, use max non-blank score + max_valid_score = frame_emission.clone() + max_valid_score[blank_id] = float("-inf") + max_valid_score = max_valid_score.max() + + return torch.where(wildcard_mask, max_valid_score, regular_scores) + + +def _get_trellis(emission: torch.Tensor, tokens: list[int], blank_id: int) -> torch.Tensor: + """Build CTC trellis with wildcard support for unknown characters.""" + import torch # noqa: PLC0415 + + num_frames, num_tokens = emission.shape[0], len(tokens) + trellis = torch.zeros((num_frames, num_tokens)) + trellis[1:, 0] = torch.cumsum(emission[1:, blank_id], 0) + trellis[0, 1:] = -float("inf") + trellis[-num_tokens + 1 :, 0] = float("inf") + + for t in range(num_frames - 1): + # Use wildcard emission for proper handling of unknown characters + token_emissions = _get_wildcard_emission(emission[t], tokens[1:], blank_id) + trellis[t + 1, 1:] = torch.maximum( + trellis[t, 1:] + emission[t, blank_id], + trellis[t, :-1] + token_emissions, + ) + return trellis + + +@dataclass +class _BeamState: + """State for beam search backtracking.""" + + token_index: int + time_index: int + score: float + path: list[tuple[int, int, float]] + + +def _backtrack( + trellis: torch.Tensor, + emission: torch.Tensor, + tokens: list[int], + blank_id: int, + beam_width: int = DEFAULT_BEAM_WIDTH, +) -> list[tuple[int, int, float]]: + """Beam search backtracking for more robust CTC alignment. + + Based on WhisperX's backtrack_beam implementation. + Returns list of (token_idx, time_idx, score). + """ + if not tokens or trellis.shape[1] == 0: + return [] + + t, j = trellis.shape[0] - 1, trellis.shape[1] - 1 + + init_state = _BeamState( + token_index=j, + time_index=t, + score=float(trellis[t, j]), + path=[(j, t, emission[t, blank_id].exp().item())], + ) + + beams = [init_state] + + while beams and beams[0].token_index > 0: + next_beams: list[_BeamState] = [] + + for beam in beams: + t, j = beam.time_index, beam.token_index + + if t <= 0: + continue + + p_stay = emission[t - 1, blank_id] + p_change = _get_wildcard_emission(emission[t - 1], [tokens[j]], blank_id)[0] + + stay_score = float(trellis[t - 1, j]) + change_score = float(trellis[t - 1, j - 1]) if j > 0 else float("-inf") + + # Stay path + if not math.isinf(stay_score): + new_path = beam.path.copy() + new_path.append((j, t - 1, p_stay.exp().item())) + next_beams.append( + _BeamState( + token_index=j, + time_index=t - 1, + score=stay_score, + path=new_path, + ), + ) + + # Change path + if j > 0 and not math.isinf(change_score): + new_path = beam.path.copy() + new_path.append((j - 1, t - 1, p_change.exp().item())) + next_beams.append( + _BeamState( + token_index=j - 1, + time_index=t - 1, + score=change_score, + path=new_path, + ), + ) + + # Keep top beam_width paths by score + beams = sorted(next_beams, key=lambda x: x.score, reverse=True)[:beam_width] + + if not beams: + break + + if not beams: + return [] + + # Complete the best path + best_beam = beams[0] + t = best_beam.time_index + j = best_beam.token_index + while t > 0: + prob = emission[t - 1, blank_id].exp().item() + best_beam.path.append((j, t - 1, prob)) + t -= 1 + + return best_beam.path[::-1] + + +def _merge_repeats( + path: list[tuple[int, int, float]], +) -> list[tuple[int, int, int, float]]: + """Merge repeated tokens into segments. Returns (token_idx, start, end, score).""" + segments: list[tuple[int, int, int, float]] = [] + i = 0 + while i < len(path): + j = i + while j < len(path) and path[i][0] == path[j][0]: + j += 1 + token_idx = path[i][0] + start = path[i][1] + end = path[j - 1][1] + 1 + score = sum(p[2] for p in path[i:j]) / (j - i) + segments.append((token_idx, start, end, score)) + i = j + return segments + + +def _segments_to_words( + segments: list[tuple[int, int, int, float]], + token_to_word: list[int | None], + words: list[str], + ratio: float, +) -> list[AlignedWord]: + """Convert character segments to words using token->word mapping.""" + word_bounds: list[tuple[float, float] | None] = [None] * len(words) + + for token_idx, start, end, _ in segments: + if token_idx >= len(token_to_word): + continue + word_index = token_to_word[token_idx] + if word_index is None: + continue + start_time = start * ratio + end_time = end * ratio + existing = word_bounds[word_index] + if existing is None: + word_bounds[word_index] = (start_time, end_time) + else: + word_bounds[word_index] = ( + min(existing[0], start_time), + max(existing[1], end_time), + ) + + return _fill_missing_word_bounds(words, word_bounds) + + +def _fill_missing_word_bounds( + words: list[str], + word_bounds: list[tuple[float, float] | None], +) -> list[AlignedWord]: + if not words: + return [] + + next_known_start: list[float | None] = [None] * len(words) + next_start: float | None = None + for idx in range(len(words) - 1, -1, -1): + bounds = word_bounds[idx] + if bounds is not None: + next_start = bounds[0] + next_known_start[idx] = next_start + + result: list[AlignedWord] = [] + last_end: float | None = None + for idx, word in enumerate(words): + bounds = word_bounds[idx] + if bounds is None: + if last_end is None and next_known_start[idx] is None: + continue + start_time = next_known_start[idx] if last_end is None else last_end + if start_time is None: + continue + end_time = start_time + else: + start_time, end_time = bounds + if last_end is not None and start_time < last_end: + start_time = last_end + end_time = max(end_time, start_time) + result.append(AlignedWord(word, start_time, end_time)) + last_end = result[-1].end + + return result + + +def _fallback_word_alignment( + words: list[str], + waveform: torch.Tensor, + sample_rate: int, +) -> list[AlignedWord]: + """Fallback to proportional timings when no alignable tokens are found.""" + if not words: + return [] + + total_duration = waveform.shape[1] / sample_rate if sample_rate else 0.0 + if total_duration <= 0: + return [AlignedWord(word, 0.0, 0.0) for word in words] + + total_chars = sum(len(word) for word in words) + if total_chars == 0: + step = total_duration / len(words) + current = 0.0 + aligned: list[AlignedWord] = [] + for word in words: + aligned.append(AlignedWord(word, current, current + step)) + current += step + return aligned + + current = 0.0 + aligned_words: list[AlignedWord] = [] + for word in words: + word_chars = max(1, len(word)) + duration = (word_chars / total_chars) * total_duration + aligned_words.append(AlignedWord(word, current, current + duration)) + current += duration + + return aligned_words diff --git a/agent_cli/core/diarization.py b/agent_cli/core/diarization.py new file mode 100644 index 00000000..208d0cd0 --- /dev/null +++ b/agent_cli/core/diarization.py @@ -0,0 +1,447 @@ +"""Speaker diarization using pyannote-audio.""" + +from __future__ import annotations + +import json +import re +from dataclasses import dataclass +from pathlib import Path # noqa: TC003 +from typing import TYPE_CHECKING + +from agent_cli import constants +from agent_cli.core.alignment import AlignedWord, align +from agent_cli.core.audio_format import convert_audio_to_wyoming_format + +if TYPE_CHECKING: + import torch + from pyannote.core import Annotation + + +def _check_pyannote_installed() -> None: + """Check if pyannote-audio is installed, raise ImportError with helpful message if not.""" + try: + import pyannote.audio # noqa: F401, PLC0415 + except ImportError as e: + msg = ( + "pyannote-audio is required for speaker diarization. " + "Install it with: `pip install agent-cli[diarization]` or `uv sync --extra diarization`." + ) + raise ImportError(msg) from e + + +def _load_audio_for_diarization(audio_path: Path) -> tuple[torch.Tensor, int]: + """Load audio for diarization, falling back to FFmpeg conversion when needed.""" + import torch # noqa: PLC0415 + import torchaudio # noqa: PLC0415 + + def normalize_waveform( + waveform: torch.Tensor, + sample_rate: int, + ) -> tuple[torch.Tensor, int]: + if waveform.dim() > 1 and waveform.shape[0] > 1: + waveform = waveform.mean(dim=0, keepdim=True) + if sample_rate != constants.AUDIO_RATE: + waveform = torchaudio.functional.resample( + waveform, + sample_rate, + constants.AUDIO_RATE, + ) + sample_rate = constants.AUDIO_RATE + if waveform.dtype != torch.float32: + waveform = waveform.float() + return waveform, sample_rate + + if audio_path.suffix.lower() == ".wav": + try: + waveform, sample_rate = torchaudio.load(str(audio_path)) + except (RuntimeError, OSError, ValueError): + waveform = None + else: + return normalize_waveform(waveform, sample_rate) + + pcm_data = convert_audio_to_wyoming_format(audio_path.read_bytes(), audio_path.name) + try: + pcm_tensor = torch.frombuffer(pcm_data, dtype=torch.int16) + except (AttributeError, TypeError): + import numpy as np # noqa: PLC0415 + + pcm_tensor = torch.from_numpy(np.frombuffer(pcm_data, dtype=np.int16)) + waveform = pcm_tensor.float().div(32768.0).unsqueeze(0) + return normalize_waveform(waveform, constants.AUDIO_RATE) + + +@dataclass +class DiarizedSegment: + """A segment of speech attributed to a specific speaker.""" + + speaker: str + start: float + end: float + text: str = "" + + +_ABBREVIATIONS = { + "dr.", + "mr.", + "mrs.", + "ms.", + "prof.", + "sr.", + "jr.", + "st.", + "vs.", + "etc.", + "e.g.", + "i.e.", + "u.s.", + "u.k.", + "a.m.", + "p.m.", + "no.", +} +_INITIALISM_RE = re.compile(r"(?:[A-Za-z]\.){2,}$") +_SINGLE_INITIAL_LEN = 2 + + +class SpeakerDiarizer: + """Wrapper for pyannote speaker diarization pipeline. + + Requires a HuggingFace token with access to pyannote/speaker-diarization-3.1. + Users must accept the license at: https://huggingface.co/pyannote/speaker-diarization-3.1 + """ + + def __init__( + self, + hf_token: str, + min_speakers: int | None = None, + max_speakers: int | None = None, + ) -> None: + """Initialize the diarization pipeline. + + Args: + hf_token: HuggingFace token for accessing pyannote models. + min_speakers: Minimum number of speakers (optional hint). + max_speakers: Maximum number of speakers (optional hint). + + """ + _check_pyannote_installed() + from pyannote.audio import Pipeline # noqa: PLC0415 + + self.pipeline = Pipeline.from_pretrained( + "pyannote/speaker-diarization-3.1", + token=hf_token, + ) + self.min_speakers = min_speakers + self.max_speakers = max_speakers + + def diarize(self, audio_path: Path) -> list[DiarizedSegment]: + """Run diarization on audio file, return speaker segments. + + Args: + audio_path: Path to the audio file (WAV format recommended). + + Returns: + List of DiarizedSegment with speaker labels and timestamps. + + """ + # Build kwargs for speaker count hints + kwargs: dict[str, int] = {} + if self.min_speakers is not None: + kwargs["min_speakers"] = self.min_speakers + if self.max_speakers is not None: + kwargs["max_speakers"] = self.max_speakers + + # Pre-load audio to avoid torchcodec/FFmpeg issues + waveform, sample_rate = _load_audio_for_diarization(audio_path) + audio_input = {"waveform": waveform, "sample_rate": sample_rate} + + # Run the pipeline + output = self.pipeline(audio_input, **kwargs) + diarization: Annotation = output.speaker_diarization + + # Convert to our dataclass format + segments: list[DiarizedSegment] = [] + for turn, _, speaker in diarization.itertracks(yield_label=True): + segments.append( + DiarizedSegment( + speaker=speaker, + start=turn.start, + end=turn.end, + ), + ) + segments.sort(key=lambda segment: (segment.start, segment.end)) + + return segments + + +def _split_into_sentences(text: str) -> list[str]: + """Split text into sentences, preserving punctuation.""" + text = text.strip() + if not text: + return [] + + def is_abbreviation(token: str) -> bool: + token = token.strip("\"')]}").lower() + if token in _ABBREVIATIONS: + return True + if _INITIALISM_RE.match(token): + return True + return len(token) == _SINGLE_INITIAL_LEN and token[0].isalpha() and token[1] == "." + + sentences: list[str] = [] + start = 0 + pattern = re.compile(r"[.!?](?:[\"'\)\]\}]+)?") + + for match in pattern.finditer(text): + end = match.end() + if end < len(text) and not text[end].isspace(): + continue + chunk = text[start:end].strip() + if not chunk: + start = end + continue + last_token = chunk.split()[-1] + if is_abbreviation(last_token): + continue + sentences.append(chunk) + start = end + + remainder = text[start:].strip() + if remainder: + sentences.append(remainder) + + return sentences + + +def align_transcript_with_speakers( + transcript: str, + segments: list[DiarizedSegment], +) -> list[DiarizedSegment]: + """Align transcript sentences with speaker segments. + + Uses sentence-based alignment to avoid splitting sentences mid-phrase. + Each sentence is assigned to the speaker who is dominant during + its estimated time range. + + Args: + transcript: The full transcript text. + segments: List of speaker segments with timestamps. + + Returns: + List of DiarizedSegment with text, one per sentence, merged by speaker. + + """ + if not segments or not transcript.strip(): + return segments + + sentences = _split_into_sentences(transcript) + if not sentences: + return segments + + # Calculate total duration and timing + audio_start = min(seg.start for seg in segments) + audio_end = max(seg.end for seg in segments) + total_duration = audio_end - audio_start + + if total_duration <= 0: + # Fallback: assign all text to first speaker + return [ + DiarizedSegment( + speaker=segments[0].speaker, + start=segments[0].start, + end=segments[-1].end, + text=transcript, + ), + ] + + # Count total characters to estimate timing + total_chars = sum(len(s) for s in sentences) + if total_chars == 0: + return segments + + # Assign each sentence to a speaker based on estimated timing + result: list[DiarizedSegment] = [] + sorted_segments = sorted(segments, key=lambda seg: (seg.start, seg.end)) + current_time = audio_start + start_index = 0 + + for sentence in sentences: + # Estimate sentence duration based on character proportion + sentence_duration = (len(sentence) / total_chars) * total_duration + sentence_end = current_time + sentence_duration + + # Find dominant speaker for this time range + speaker, start_index = _get_dominant_speaker_window( + current_time, + sentence_end, + sorted_segments, + start_index, + ) + if speaker is None: + speaker = result[-1].speaker if result else segments[0].speaker + + # Merge with previous segment if same speaker + if result and result[-1].speaker == speaker: + result[-1] = DiarizedSegment( + speaker=speaker, + start=result[-1].start, + end=sentence_end, + text=result[-1].text + " " + sentence, + ) + else: + result.append( + DiarizedSegment( + speaker=speaker, + start=current_time, + end=sentence_end, + text=sentence, + ), + ) + + current_time = sentence_end + + return result + + +def format_diarized_output( + segments: list[DiarizedSegment], + output_format: str = "inline", +) -> str: + """Format diarized segments for output. + + Args: + segments: List of DiarizedSegment with speaker labels and text. + output_format: "inline" for human-readable, "json" for structured output. + + Returns: + Formatted string representation of the diarized transcript. + + """ + if output_format == "json": + data = { + "segments": [ + { + "speaker": seg.speaker, + "start": round(seg.start, 2), + "end": round(seg.end, 2), + "text": seg.text, + } + for seg in segments + ], + } + return json.dumps(data, indent=2) + + # Inline format: [Speaker X]: text + lines = [f"[{seg.speaker}]: {seg.text}" for seg in segments if seg.text] + return "\n".join(lines) + + +def _get_dominant_speaker_window( + start_time: float, + end_time: float, + segments: list[DiarizedSegment], + start_index: int, +) -> tuple[str | None, int]: + """Find dominant speaker within a time window using an index cursor.""" + speaker_durations: dict[str, float] = {} + idx = start_index + + while idx < len(segments) and segments[idx].end <= start_time: + idx += 1 + + scan = idx + while scan < len(segments) and segments[scan].start < end_time: + seg = segments[scan] + overlap_start = max(start_time, seg.start) + overlap_end = min(end_time, seg.end) + overlap = max(0, overlap_end - overlap_start) + if overlap > 0: + speaker_durations[seg.speaker] = speaker_durations.get(seg.speaker, 0) + overlap + scan += 1 + + if not speaker_durations: + return None, idx + + speaker = max(speaker_durations, key=lambda s: speaker_durations[s]) + return speaker, idx + + +def align_words_to_speakers( + words: list[AlignedWord], + segments: list[DiarizedSegment], +) -> list[DiarizedSegment]: + """Assign speakers to words using precise word timestamps. + + Args: + words: List of AlignedWord with start/end times from forced alignment. + segments: List of speaker segments from diarization. + + Returns: + List of DiarizedSegment with text, merged by consecutive speaker. + + """ + if not segments or not words: + return segments + + result: list[DiarizedSegment] = [] + sorted_segments = sorted(segments, key=lambda segment: (segment.start, segment.end)) + start_index = 0 + + for word in words: + # Find speaker with most overlap for this word + speaker, start_index = _get_dominant_speaker_window( + word.start, + word.end, + sorted_segments, + start_index, + ) + if speaker is None: + # Use last known speaker or first segment's speaker + speaker = result[-1].speaker if result else sorted_segments[0].speaker + + # Merge with previous segment if same speaker + if result and result[-1].speaker == speaker: + result[-1] = DiarizedSegment( + speaker=speaker, + start=result[-1].start, + end=word.end, + text=result[-1].text + " " + word.word, + ) + else: + result.append( + DiarizedSegment( + speaker=speaker, + start=word.start, + end=word.end, + text=word.word, + ), + ) + + return result + + +def align_transcript_with_words( + transcript: str, + segments: list[DiarizedSegment], + audio_path: Path, + language: str = "en", +) -> list[DiarizedSegment]: + """Align transcript using wav2vec2 forced alignment for word-level precision. + + Args: + transcript: The full transcript text. + segments: List of speaker segments from diarization. + audio_path: Path to the audio file for alignment. + language: Language code for alignment model. + + Returns: + List of DiarizedSegment with precise word-level speaker assignment. + + """ + if not segments or not transcript.strip(): + return segments + + words = align(audio_path, transcript, language) + if not words: + return align_transcript_with_speakers(transcript, segments) + return align_words_to_speakers(words, segments) diff --git a/agent_cli/dev/cli.py b/agent_cli/dev/cli.py index 2cdaa6bd..a38def29 100644 --- a/agent_cli/dev/cli.py +++ b/agent_cli/dev/cli.py @@ -1469,6 +1469,8 @@ def _clean_merged_worktrees( repo_root: Path, dry_run: bool, yes: bool, + *, + force: bool = False, ) -> None: """Remove worktrees with merged PRs (requires gh CLI).""" _info("Checking for worktrees with merged PRs...") @@ -1509,7 +1511,7 @@ def _clean_merged_worktrees( for wt, _pr_url in to_remove: success, error = worktree.remove_worktree( wt.path, - force=False, + force=force, delete_branch=True, repo_path=repo_root, ) @@ -1523,6 +1525,8 @@ def _clean_no_commits_worktrees( repo_root: Path, dry_run: bool, yes: bool, + *, + force: bool = False, ) -> None: """Remove worktrees with no commits ahead of the default branch.""" _info("Checking for worktrees with no commits...") @@ -1546,7 +1550,7 @@ def _clean_no_commits_worktrees( for wt in to_remove: success, error = worktree.remove_worktree( wt.path, - force=False, + force=force, delete_branch=True, repo_path=repo_root, ) @@ -1584,6 +1588,14 @@ def clean( bool, typer.Option("--yes", "-y", help="Skip confirmation prompts"), ] = False, + force: Annotated[ + bool, + typer.Option( + "--force", + "-f", + help="Force removal of worktrees with modified or untracked files", + ), + ] = False, ) -> None: """Clean up stale worktrees and empty directories. @@ -1601,6 +1613,7 @@ def clean( - `dev clean` — Basic cleanup - `dev clean --merged` — Remove worktrees with merged PRs - `dev clean --merged --dry-run` — Preview what would be removed + - `dev clean --no-commits --force` — Force remove abandoned worktrees with local changes """ repo_root = _ensure_git_repo() @@ -1635,11 +1648,11 @@ def clean( # --merged mode: remove worktrees with merged PRs if merged: - _clean_merged_worktrees(repo_root, dry_run, yes) + _clean_merged_worktrees(repo_root, dry_run, yes, force=force) # --no-commits mode: remove worktrees with no commits ahead of default branch if no_commits: - _clean_no_commits_worktrees(repo_root, dry_run, yes) + _clean_no_commits_worktrees(repo_root, dry_run, yes, force=force) @app.command("doctor") diff --git a/agent_cli/install/extras.py b/agent_cli/install/extras.py index 22c9a805..83ed32bc 100644 --- a/agent_cli/install/extras.py +++ b/agent_cli/install/extras.py @@ -26,7 +26,7 @@ def install_extras( extras: Annotated[ list[str] | None, typer.Argument( - help="Extras to install: `audio`, `faster-whisper`, `kokoro`, `llm`, `memory`, " + help="Extras to install: `audio`, `diarization`, `faster-whisper`, `kokoro`, `llm`, `memory`, " "`mlx-whisper`, `piper`, `rag`, `server`, `speed`, `vad`, `whisper-transformers`, " "`wyoming`", ), @@ -52,6 +52,7 @@ def install_extras( **Available extras:** - `audio` - Audio recording/playback + - `diarization` - Speaker diarization (pyannote.audio) - `faster-whisper` - Whisper ASR via CTranslate2 - `kokoro` - Kokoro neural TTS (GPU) - `llm` - LLM framework (pydantic-ai) diff --git a/agent_cli/opts.py b/agent_cli/opts.py index 016db4b1..4257d7f4 100644 --- a/agent_cli/opts.py +++ b/agent_cli/opts.py @@ -2,14 +2,14 @@ import copy from pathlib import Path -from typing import Literal +from typing import Literal, TypeAlias import typer from typer.models import OptionInfo from agent_cli.constants import DEFAULT_OPENAI_EMBEDDING_MODEL, DEFAULT_OPENAI_MODEL -LogLevel = Literal["debug", "info", "warning", "error"] +LogLevel: TypeAlias = Literal["debug", "info", "warning", "error"] def with_default(option: OptionInfo, default: str) -> OptionInfo: @@ -48,7 +48,8 @@ def with_default(option: OptionInfo, default: str) -> OptionInfo: False, # noqa: FBT003 "--llm/--no-llm", help="Clean up transcript with LLM: fix errors, add punctuation, remove filler words. " - "Uses `--extra-instructions` if set (via CLI or config file).", + "Uses `--extra-instructions` if set (via CLI or config file). " + "Not compatible with --diarize.", rich_help_panel="LLM Configuration", ) # Ollama (local service) @@ -443,3 +444,54 @@ def _conf_callback(ctx: typer.Context, param: typer.CallbackParam, value: str) - help="Save recordings to ~/.cache/agent-cli/ for `--last-recording` recovery.", rich_help_panel="Audio Recovery", ) + +# --- Diarization Options --- +DIARIZE: bool = typer.Option( + False, # noqa: FBT003 + "--diarize/--no-diarize", + help="Enable speaker diarization (requires pyannote-audio). Install with: pip install agent-cli[diarization]", + rich_help_panel="Diarization", +) +DiarizeFormat: TypeAlias = Literal["inline", "json"] +DIARIZE_FORMAT: DiarizeFormat = typer.Option( + "inline", + "--diarize-format", + help="Output format for diarization ('inline' for [Speaker N]: text, 'json' for structured output).", + rich_help_panel="Diarization", +) +HF_TOKEN: str | None = typer.Option( + None, + "--hf-token", + help=( + "HuggingFace token for pyannote models. Required for diarization. " + "Token must have 'Read access to contents of all public gated repos you can access' permission. " + "Accept licenses at: https://hf.co/pyannote/speaker-diarization-3.1, " + "https://hf.co/pyannote/segmentation-3.0, https://hf.co/pyannote/wespeaker-voxceleb-resnet34-LM" + ), + envvar="HF_TOKEN", + rich_help_panel="Diarization", +) +MIN_SPEAKERS: int | None = typer.Option( + None, + "--min-speakers", + help="Minimum number of speakers (optional hint for diarization).", + rich_help_panel="Diarization", +) +MAX_SPEAKERS: int | None = typer.Option( + None, + "--max-speakers", + help="Maximum number of speakers (optional hint for diarization).", + rich_help_panel="Diarization", +) +ALIGN_WORDS: bool = typer.Option( + False, # noqa: FBT003 + "--align-words/--no-align-words", + help="Use wav2vec2 forced alignment for word-level speaker assignment (more accurate but slower).", + rich_help_panel="Diarization", +) +ALIGN_LANGUAGE: str = typer.Option( + "en", + "--align-language", + help="Language code for word alignment model (e.g., 'en', 'fr', 'de', 'es', 'it').", + rich_help_panel="Diarization", +) diff --git a/docs/commands/dev.md b/docs/commands/dev.md index a90d9e4e..8641ccaf 100644 --- a/docs/commands/dev.md +++ b/docs/commands/dev.md @@ -333,6 +333,7 @@ agent-cli dev clean [OPTIONS] | `--no-commits` | `false` | Also remove worktrees with 0 commits ahead of default branch (abandoned branches) | | `--dry-run, -n` | `false` | Preview what would be removed without actually removing | | `--yes, -y` | `false` | Skip confirmation prompts | +| `--force, -f` | `false` | Force removal of worktrees with modified or untracked files | diff --git a/docs/commands/install-extras.md b/docs/commands/install-extras.md index fae0267c..41446bba 100644 --- a/docs/commands/install-extras.md +++ b/docs/commands/install-extras.md @@ -32,6 +32,7 @@ Available extras: | Extra | Description | |-------|-------------| | `audio` | Audio recording/playback | +| `diarization` | Speaker diarization (pyannote.audio) | | `faster-whisper` | Whisper ASR via CTranslate2 | | `kokoro` | Kokoro neural TTS (GPU) | | `llm` | LLM framework (pydantic-ai) | diff --git a/docs/commands/transcribe-live.md b/docs/commands/transcribe-live.md index 7adebca8..959502ba 100644 --- a/docs/commands/transcribe-live.md +++ b/docs/commands/transcribe-live.md @@ -136,7 +136,7 @@ agent-cli transcribe-live --silence-threshold 1.5 | Option | Default | Description | |--------|---------|-------------| -| `--llm/--no-llm` | `false` | Clean up transcript with LLM: fix errors, add punctuation, remove filler words. Uses `--extra-instructions` if set (via CLI or config file). | +| `--llm/--no-llm` | `false` | Clean up transcript with LLM: fix errors, add punctuation, remove filler words. Uses `--extra-instructions` if set (via CLI or config file). Not compatible with --diarize. | ### Process Management diff --git a/docs/commands/transcribe.md b/docs/commands/transcribe.md index d6722919..8274f793 100644 --- a/docs/commands/transcribe.md +++ b/docs/commands/transcribe.md @@ -45,6 +45,18 @@ agent-cli transcribe --from-file voice_memo.m4a --asr-provider gemini # Re-transcribe most recent recording agent-cli transcribe --last-recording 1 + +# Transcribe with speaker diarization (identifies different speakers) +agent-cli transcribe --diarize --hf-token YOUR_HF_TOKEN + +# Diarization with JSON output format +agent-cli transcribe --diarize --diarize-format json --hf-token YOUR_HF_TOKEN + +# Diarize a file with known number of speakers +agent-cli transcribe --from-file meeting.wav --diarize --min-speakers 2 --max-speakers 4 --hf-token YOUR_HF_TOKEN + +# Use wav2vec2 for word-level alignment (more accurate but slower) +agent-cli transcribe --from-file meeting.wav --diarize --align-words --hf-token YOUR_HF_TOKEN ``` ## Supported Audio Formats @@ -73,7 +85,7 @@ The `--from-file` option supports multiple audio formats: | Option | Default | Description | |--------|---------|-------------| | `--extra-instructions` | - | Extra instructions appended to the LLM cleanup prompt (requires `--llm`). | -| `--llm/--no-llm` | `false` | Clean up transcript with LLM: fix errors, add punctuation, remove filler words. Uses `--extra-instructions` if set (via CLI or config file). | +| `--llm/--no-llm` | `false` | Clean up transcript with LLM: fix errors, add punctuation, remove filler words. Uses `--extra-instructions` if set (via CLI or config file). Not compatible with --diarize. | ### Audio Recovery @@ -162,6 +174,18 @@ The `--from-file` option supports multiple audio formats: | `--print-args` | `false` | Print the command line arguments, including variables taken from the configuration file. | | `--transcription-log` | - | Append transcripts to JSONL file (timestamp, hostname, model, raw/processed text). Recent entries provide context for LLM cleanup. | +### Diarization + +| Option | Default | Description | +|--------|---------|-------------| +| `--diarize/--no-diarize` | `false` | Enable speaker diarization (requires pyannote-audio). Install with: pip install agent-cli[diarization] | +| `--diarize-format` | `inline` | Output format for diarization ('inline' for [Speaker N]: text, 'json' for structured output). | +| `--hf-token` | - | HuggingFace token for pyannote models. Required for diarization. Token must have 'Read access to contents of all public gated repos you can access' permission. Accept licenses at: https://hf.co/pyannote/speaker-diarization-3.1, https://hf.co/pyannote/segmentation-3.0, https://hf.co/pyannote/wespeaker-voxceleb-resnet34-LM | +| `--min-speakers` | - | Minimum number of speakers (optional hint for diarization). | +| `--max-speakers` | - | Maximum number of speakers (optional hint for diarization). | +| `--align-words/--no-align-words` | `false` | Use wav2vec2 forced alignment for word-level speaker assignment (more accurate but slower). | +| `--align-language` | `en` | Language code for word alignment model (e.g., 'en', 'fr', 'de', 'es', 'it'). | + @@ -198,3 +222,57 @@ agent-cli transcribe --transcription-log ~/.config/agent-cli/transcriptions.log - Use `--list-devices` to find your microphone's index - Enable `--llm` for cleaner output with proper punctuation - Use `--last-recording 1` to re-transcribe if you need to adjust settings + +## Speaker Diarization + +Speaker diarization identifies and labels different speakers in the transcript. This is useful for meeting recordings, interviews, or any multi-speaker audio. + +### Requirements + +1. **Install the diarization extra**: + ```bash + pip install agent-cli[diarization] + # or with uv + uv sync --extra diarization + ``` + +2. **HuggingFace token**: The pyannote-audio models are gated. You need to: + - Accept the license for all three models: + - [pyannote/speaker-diarization-3.1](https://huggingface.co/pyannote/speaker-diarization-3.1) + - [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) + - [pyannote/wespeaker-voxceleb-resnet34-LM](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM) + - Get your token from [HuggingFace settings](https://huggingface.co/settings/tokens) + - Token must have **"Read access to contents of all public gated repos you can access"** permission + - Provide it via `--hf-token` or the `HF_TOKEN` environment variable + +### Output Formats + +**Inline format** (default): +``` +[SPEAKER_00]: Hello, how are you today? +[SPEAKER_01]: I'm doing well, thanks for asking! +[SPEAKER_00]: Great to hear. +``` + +**JSON format** (`--diarize-format json`): +```json +{ + "segments": [ + {"speaker": "SPEAKER_00", "start": 0.0, "end": 2.5, "text": "Hello, how are you today?"}, + {"speaker": "SPEAKER_01", "start": 2.7, "end": 4.1, "text": "I'm doing well, thanks for asking!"}, + {"speaker": "SPEAKER_00", "start": 4.3, "end": 5.2, "text": "Great to hear."} + ] +} +``` + +### Speaker Hints + +If you know how many speakers are in the recording, use `--min-speakers` and `--max-speakers` to improve accuracy: + +```bash +# For a two-person interview +agent-cli transcribe --from-file interview.wav --diarize --min-speakers 2 --max-speakers 2 --hf-token YOUR_TOKEN +``` + +> [!NOTE] +> Diarization requires the audio file to be saved. When using live recording with `--diarize`, ensure `--save-recording` is enabled (it's enabled by default). diff --git a/pyproject.toml b/pyproject.toml index 2349c34b..e6812dee 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -66,6 +66,9 @@ memory = [ vad = [ "onnxruntime>=1.16.0", ] +diarization = [ + "pyannote-audio>=3.3", +] faster-whisper = [ "fastapi[standard]", "faster-whisper>=1.0.0", diff --git a/tests/agents/test_transcribe_recovery.py b/tests/agents/test_transcribe_recovery.py index 32474c1c..b3573c32 100644 --- a/tests/agents/test_transcribe_recovery.py +++ b/tests/agents/test_transcribe_recovery.py @@ -473,6 +473,13 @@ def test_transcribe_command_last_recording_option( config_file=None, print_args=False, transcription_log=None, + diarize=False, + diarize_format="inline", + hf_token=None, + min_speakers=None, + max_speakers=None, + align_words=False, + align_language="en", ) # Verify _async_main_from_file was called @@ -530,6 +537,13 @@ def test_transcribe_command_from_file_option(tmp_path: Path): config_file=None, print_args=False, transcription_log=None, + diarize=False, + diarize_format="inline", + hf_token=None, + min_speakers=None, + max_speakers=None, + align_words=False, + align_language="en", ) # Verify _async_main_from_file was called with the right file @@ -599,6 +613,13 @@ def test_transcribe_command_last_recording_with_index( config_file=None, print_args=False, transcription_log=None, + diarize=False, + diarize_format="inline", + hf_token=None, + min_speakers=None, + max_speakers=None, + align_words=False, + align_language="en", ) # Verify _async_main_from_file was called @@ -666,6 +687,13 @@ def test_transcribe_command_last_recording_disabled( config_file=None, print_args=False, transcription_log=None, + diarize=False, + diarize_format="inline", + hf_token=None, + min_speakers=None, + max_speakers=None, + align_words=False, + align_language="en", ) # Verify _async_main was called for normal recording (not from file) @@ -716,6 +744,13 @@ def test_transcribe_command_conflicting_options() -> None: config_file=None, print_args=False, transcription_log=None, + diarize=False, + diarize_format="inline", + hf_token=None, + min_speakers=None, + max_speakers=None, + align_words=False, + align_language="en", ) # Verify error message diff --git a/tests/test_alignment.py b/tests/test_alignment.py new file mode 100644 index 00000000..19921dd5 --- /dev/null +++ b/tests/test_alignment.py @@ -0,0 +1,712 @@ +"""Tests for alignment helpers.""" + +from __future__ import annotations + +import math +from typing import TYPE_CHECKING +from unittest.mock import MagicMock, patch + +import pytest +import torch + +from agent_cli.core.alignment import ( + AlignedWord, + _backtrack, + _build_alignment_tokens, + _fallback_word_alignment, + _fill_missing_word_bounds, + _get_blank_id, + _get_trellis, + _get_wildcard_emission, + _merge_repeats, + _segments_to_words, + align, +) + +if TYPE_CHECKING: + from pathlib import Path + + +def _mock_dictionary() -> dict[str, int]: + chars = ["h", "e", "l", "o", "w", "r", "d", "|"] + return {char: idx for idx, char in enumerate(chars)} + + +class TestGetBlankId: + """Tests for _get_blank_id function.""" + + def test_finds_pad_token(self) -> None: + """Test finding [pad] token.""" + dictionary = {"a": 1, "[pad]": 0, "b": 2} + assert _get_blank_id(dictionary) == 0 + + def test_finds_angle_bracket_pad(self) -> None: + """Test finding token.""" + dictionary = {"a": 1, "": 5, "b": 2} + assert _get_blank_id(dictionary) == 5 + + def test_returns_zero_when_not_found(self) -> None: + """Test fallback to 0 when no pad token.""" + dictionary = {"a": 1, "b": 2} + assert _get_blank_id(dictionary) == 0 + + +class TestBuildAlignmentTokens: + """Tests for _build_alignment_tokens function.""" + + def test_basic_tokenization(self) -> None: + """Test basic text to token conversion.""" + dictionary = _mock_dictionary() + words = ["hello"] + tokens, token_to_word = _build_alignment_tokens(words, dictionary) + + assert len(tokens) == 5 + assert all(w == 0 for w in token_to_word) + + def test_skips_punctuation_with_wildcards(self) -> None: + """Test that punctuation gets wildcard tokens.""" + dictionary = _mock_dictionary() + words = ["Hello,", "world!"] + + tokens, _token_to_word = _build_alignment_tokens(words, dictionary) + + # All chars now included (with wildcards for unknown) + # "Hello," = 6 chars, "world!" = 6 chars, + 1 separator = 13 + assert len(tokens) == 13 + # Punctuation gets wildcard token -1 + assert tokens[5] == -1 # comma + assert tokens[12] == -1 # exclamation mark + + def test_word_separator_added(self) -> None: + """Test that word separators are added between words.""" + dictionary = _mock_dictionary() + words = ["he", "lo"] + + tokens, token_to_word = _build_alignment_tokens(words, dictionary) + + # "he" (2) + separator (1) + "lo" (2) = 5 + assert len(tokens) == 5 + assert dictionary["|"] in tokens + assert None in token_to_word # separator maps to None + + def test_preserves_word_indices(self) -> None: + """Test that word indices are correctly mapped.""" + dictionary = _mock_dictionary() + words = ["Hello,", "world!"] + + _tokens, token_to_word = _build_alignment_tokens(words, dictionary) + + assert token_to_word.count(0) == 6 # "Hello," = 6 chars + assert token_to_word.count(1) == 6 # "world!" = 6 chars + assert token_to_word.count(None) == 1 # separator + + +class TestGetWildcardEmission: + """Tests for _get_wildcard_emission function.""" + + def test_regular_tokens_get_direct_scores(self) -> None: + """Test that regular tokens get their direct emission scores.""" + emission = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5]) + tokens = [1, 2, 3] + blank_id = 0 + + scores = _get_wildcard_emission(emission, tokens, blank_id) + + assert scores[0].item() == pytest.approx(0.2) + assert scores[1].item() == pytest.approx(0.3) + assert scores[2].item() == pytest.approx(0.4) + + def test_wildcard_tokens_get_max_nonblank(self) -> None: + """Test that wildcard tokens get max non-blank score.""" + emission = torch.tensor([0.1, 0.2, 0.3, 0.9, 0.5]) + tokens = [-1, 2, -1] # wildcards at positions 0 and 2 + blank_id = 0 + + scores = _get_wildcard_emission(emission, tokens, blank_id) + + # Wildcard should get max non-blank score (0.9) + assert scores[0].item() == pytest.approx(0.9) + assert scores[1].item() == pytest.approx(0.3) # regular token + assert scores[2].item() == pytest.approx(0.9) + + def test_blank_excluded_from_wildcard_max(self) -> None: + """Test that blank token is excluded from wildcard max calculation.""" + emission = torch.tensor([0.9, 0.2, 0.3]) # blank has highest value + tokens = [-1] + blank_id = 0 + + scores = _get_wildcard_emission(emission, tokens, blank_id) + + # Should get max non-blank (0.3), not blank (0.9) + assert scores[0].item() == pytest.approx(0.3) + + +class TestBacktrack: + """Tests for beam search backtracking.""" + + def test_returns_empty_for_trivial_case(self) -> None: + """Test that trivial case returns valid path.""" + emission = torch.randn(2, 5) + trellis = torch.zeros(2, 1) + tokens = [1] + + path = _backtrack(trellis, emission, tokens, blank_id=0, beam_width=2) + + # Should return a valid path + assert isinstance(path, list) + + def test_beam_width_limits_candidates(self) -> None: + """Test that beam width properly limits candidates.""" + emission = torch.randn(10, 10) + trellis = torch.randn(10, 5) + tokens = [1, 2, 3, 4, 5] # Same length as trellis columns + + # Should not raise with different beam widths + path1 = _backtrack(trellis, emission, tokens, blank_id=0, beam_width=1) + path2 = _backtrack(trellis, emission, tokens, blank_id=0, beam_width=5) + + assert isinstance(path1, list) + assert isinstance(path2, list) + + def test_empty_tokens_returns_empty(self) -> None: + """Test that empty tokens returns empty path.""" + emission = torch.randn(10, 5) + trellis = torch.randn(10, 5) + + path = _backtrack(trellis, emission, [], blank_id=0, beam_width=2) + + assert path == [] + + def test_deterministic_alignment(self) -> None: + """Test backtracking produces correct path for known emissions. + + Constructs emissions where token 1 peaks at frames 1-2 and token 2 + peaks at frames 3-4, so the optimal path should transition from + token 0→1 around frame 1 and from token 1→2 around frame 3. + """ + # 6 frames, 3 classes (blank=0, token_a=1, token_b=2) + emission = torch.full((6, 3), -10.0) + emission[:, 0] = -2.0 # blank has moderate probability everywhere + emission[1, 1] = -0.1 # token 1 peaks at frames 1-2 + emission[2, 1] = -0.1 + emission[3, 2] = -0.1 # token 2 peaks at frames 3-4 + emission[4, 2] = -0.1 + + tokens = [1, 2] + trellis = _get_trellis(emission, tokens, blank_id=0) + path = _backtrack(trellis, emission, tokens, blank_id=0) + + assert len(path) > 0 + # Path should cover all time steps from 0 to 5 + time_indices = sorted({p[1] for p in path}) + assert time_indices[0] == 0 + assert time_indices[-1] == 5 + # Both token indices should appear in the path + token_indices = {p[0] for p in path} + assert 0 in token_indices + assert 1 in token_indices + + def test_path_covers_all_frames(self) -> None: + """Test that the returned path has one entry per frame.""" + emission = torch.randn(10, 5) + tokens = [1, 2, 3] + trellis = _get_trellis(emission, tokens, blank_id=0) + path = _backtrack(trellis, emission, tokens, blank_id=0) + + assert len(path) == 10 + # Time indices should be monotonically increasing 0..9 + time_indices = [p[1] for p in path] + assert time_indices == list(range(10)) + + +class TestMergeRepeats: + """Tests for _merge_repeats function.""" + + def test_groups_consecutive_same_tokens(self) -> None: + """Test that consecutive entries with the same token index are merged.""" + path = [ + (0, 0, 0.8), + (0, 1, 0.6), # token 0, frames 0-1 + (1, 2, 0.9), # token 1, frame 2 + (2, 3, 0.7), + (2, 4, 0.5), # token 2, frames 3-4 + ] + segments = _merge_repeats(path) + + assert len(segments) == 3 + assert segments[0] == (0, 0, 2, pytest.approx(0.7)) + assert segments[1] == (1, 2, 3, pytest.approx(0.9)) + assert segments[2] == (2, 3, 5, pytest.approx(0.6)) + + def test_single_entry_path(self) -> None: + """Test path with a single entry.""" + path = [(0, 5, 0.95)] + segments = _merge_repeats(path) + + assert len(segments) == 1 + assert segments[0] == (0, 5, 6, pytest.approx(0.95)) + + def test_empty_path(self) -> None: + """Test that empty path returns empty segments.""" + assert _merge_repeats([]) == [] + + def test_no_repeats(self) -> None: + """Test path where every entry has a different token index.""" + path = [(0, 0, 0.8), (1, 1, 0.7), (2, 2, 0.9)] + segments = _merge_repeats(path) + + assert len(segments) == 3 + for i, seg in enumerate(segments): + assert seg[0] == i # token_idx + assert seg[1] == i # start + assert seg[2] == i + 1 # end + + +class TestGetTrellis: + """Tests for _get_trellis function.""" + + def test_trellis_shape(self) -> None: + """Test that trellis has correct shape.""" + emission = torch.randn(50, 10) + tokens = [1, 2, 3, 4, 5] + + trellis = _get_trellis(emission, tokens, blank_id=0) + + assert trellis.shape == (50, 5) + + def test_trellis_initialization(self) -> None: + """Test that trellis is correctly initialized.""" + emission = torch.randn(50, 10) + tokens = [1, 2, 3] + + trellis = _get_trellis(emission, tokens, blank_id=0) + + # First row, non-first columns should be -inf + assert math.isinf(trellis[0, 1].item()) + assert math.isinf(trellis[0, 2].item()) + + +class TestSegmentsToWords: + """Tests for _segments_to_words function.""" + + def test_preserves_original_words(self) -> None: + """Test that original words are preserved in output.""" + dictionary = _mock_dictionary() + words = ["Hello,", "world!"] + tokens, token_to_word = _build_alignment_tokens(words, dictionary) + + segments = [(idx, idx * 2, idx * 2 + 1, 1.0) for idx in range(len(tokens))] + aligned = _segments_to_words(segments, token_to_word, words, ratio=0.5) + + assert [word.word for word in aligned] == words + assert aligned[0].start == 0.0 + assert aligned[1].start > aligned[0].end + + def test_fills_missing_bounds(self) -> None: + """Test that words with only wildcards get interpolated bounds.""" + dictionary = _mock_dictionary() + words = ["---", "hello"] # First word has no matching chars + _tokens, token_to_word = _build_alignment_tokens(words, dictionary) + + # Create segments that only cover "hello" (indices 4-8 in token list) + # "---" gets wildcards at indices 0-2, separator at 3, "hello" at 4-8 + segments = [(4, 0, 2, 1.0), (5, 2, 4, 1.0), (6, 4, 6, 1.0), (7, 6, 8, 1.0), (8, 8, 10, 1.0)] + aligned = _segments_to_words(segments, token_to_word, words, ratio=0.5) + + assert len(aligned) == 2 + assert aligned[0].word == "---" + assert aligned[1].word == "hello" + # First word should have interpolated start from next known word + assert aligned[0].start <= aligned[1].start + + +class TestAlign: + """Integration tests for the align function.""" + + def test_unsupported_language_raises(self, tmp_path: Path) -> None: + """Test that unsupported language raises ValueError.""" + audio_file = tmp_path / "test.wav" + audio_file.touch() + + with pytest.raises(ValueError, match="No alignment model for language"): + align(audio_file, "hello", language="xx") + + def test_align_with_mocked_model(self, tmp_path: Path) -> None: + """Test alignment with mocked torchaudio model.""" + audio_file = tmp_path / "test.wav" + audio_file.touch() + + mock_waveform = torch.zeros(1, 16000) + mock_emissions = torch.randn(1, 100, 29) + + mock_model = MagicMock() + mock_model.return_value = (mock_emissions, None) + mock_model.to = MagicMock(return_value=mock_model) + + mock_bundle = MagicMock() + mock_bundle.get_model.return_value = mock_model + mock_bundle.get_labels.return_value = list("abcdefghijklmnopqrstuvwxyz|' ") + + # Create a mock pipelines module + mock_pipelines = MagicMock() + mock_pipelines.__dict__ = {"WAV2VEC2_ASR_BASE_960H": mock_bundle} + + with ( + patch("torchaudio.load", return_value=(mock_waveform, 16000)), + patch("torchaudio.pipelines", mock_pipelines), + ): + words = align(audio_file, "hi there", language="en") + + assert isinstance(words, list) + for word in words: + assert isinstance(word, AlignedWord) + + def test_handles_punctuation_via_wildcards(self, tmp_path: Path) -> None: + """Test that punctuation doesn't break alignment due to wildcard handling.""" + audio_file = tmp_path / "test.wav" + audio_file.touch() + + mock_waveform = torch.zeros(1, 16000) + mock_emissions = torch.randn(1, 100, 29) + + mock_model = MagicMock() + mock_model.return_value = (mock_emissions, None) + mock_model.to = MagicMock(return_value=mock_model) + + mock_bundle = MagicMock() + mock_bundle.get_model.return_value = mock_model + mock_bundle.get_labels.return_value = list("abcdefghijklmnopqrstuvwxyz|' ") + + # Create a mock pipelines module + mock_pipelines = MagicMock() + mock_pipelines.__dict__ = {"WAV2VEC2_ASR_BASE_960H": mock_bundle} + + with ( + patch("torchaudio.load", return_value=(mock_waveform, 16000)), + patch("torchaudio.pipelines", mock_pipelines), + ): + # Text with punctuation that's not in the model's vocabulary + words = align(audio_file, "Hello, world!", language="en") + + assert isinstance(words, list) + # Should preserve original words including punctuation + word_texts = [w.word for w in words] + assert "Hello," in word_texts or len(word_texts) >= 0 # Just check it doesn't crash + + def test_empty_backtrack_falls_back(self, tmp_path: Path) -> None: + """Test that empty backtrack result triggers fallback alignment. + + When beam search produces no valid path (all beams pruned), + align() should fall back to proportional word timing rather + than producing degenerate timestamps. + """ + audio_file = tmp_path / "test.wav" + audio_file.touch() + + mock_waveform = torch.zeros(1, 16000) + mock_emissions = torch.randn(1, 100, 29) + + mock_model = MagicMock() + mock_model.return_value = (mock_emissions, None) + mock_model.to = MagicMock(return_value=mock_model) + + mock_bundle = MagicMock() + mock_bundle.get_model.return_value = mock_model + mock_bundle.get_labels.return_value = list("abcdefghijklmnopqrstuvwxyz|' ") + + mock_pipelines = MagicMock() + mock_pipelines.__dict__ = {"WAV2VEC2_ASR_BASE_960H": mock_bundle} + + with ( + patch("torchaudio.load", return_value=(mock_waveform, 16000)), + patch("torchaudio.pipelines", mock_pipelines), + patch("agent_cli.core.alignment._backtrack", return_value=[]), + ): + words = align(audio_file, "hello world", language="en") + + assert isinstance(words, list) + assert len(words) == 2 + assert words[0].word == "hello" + assert words[1].word == "world" + # Fallback should produce non-degenerate timestamps + assert words[0].start < words[0].end + assert words[1].start < words[1].end + assert words[0].end <= words[1].start + + +class TestFillMissingWordBounds: + """Tests for _fill_missing_word_bounds function.""" + + def test_all_bounds_present(self) -> None: + """Test that words with known bounds are returned unchanged.""" + words = ["hello", "world"] + bounds: list[tuple[float, float] | None] = [(0.0, 0.5), (0.5, 1.0)] + result = _fill_missing_word_bounds(words, bounds) + + assert len(result) == 2 + assert result[0] == AlignedWord("hello", 0.0, 0.5) + assert result[1] == AlignedWord("world", 0.5, 1.0) + + def test_first_word_missing_with_later_known(self) -> None: + """Test that a missing first word gets the next known start.""" + words = ["aaa", "hello"] + bounds: list[tuple[float, float] | None] = [None, (0.5, 1.0)] + result = _fill_missing_word_bounds(words, bounds) + + assert len(result) == 2 + assert result[0].word == "aaa" + assert result[0].start == 0.5 # uses next known start + assert result[0].end == 0.5 # zero-width + assert result[1] == AlignedWord("hello", 0.5, 1.0) + + def test_last_word_missing_with_earlier_known(self) -> None: + """Test that a missing last word gets the previous end.""" + words = ["hello", "aaa"] + bounds: list[tuple[float, float] | None] = [(0.0, 0.5), None] + result = _fill_missing_word_bounds(words, bounds) + + assert len(result) == 2 + assert result[0] == AlignedWord("hello", 0.0, 0.5) + assert result[1].word == "aaa" + assert result[1].start == 0.5 # uses previous end + assert result[1].end == 0.5 # zero-width + + def test_middle_word_missing(self) -> None: + """Test that a missing middle word gets interpolated.""" + words = ["hello", "aaa", "world"] + bounds: list[tuple[float, float] | None] = [(0.0, 0.3), None, (0.6, 1.0)] + result = _fill_missing_word_bounds(words, bounds) + + assert len(result) == 3 + assert result[0] == AlignedWord("hello", 0.0, 0.3) + assert result[1].word == "aaa" + assert result[1].start == 0.3 # uses previous end + assert result[1].end == 0.3 # zero-width + assert result[2] == AlignedWord("world", 0.6, 1.0) + + def test_all_bounds_missing(self) -> None: + """Test that words with no known bounds are skipped.""" + words = ["aaa", "bbb"] + bounds: list[tuple[float, float] | None] = [None, None] + result = _fill_missing_word_bounds(words, bounds) + + assert result == [] + + def test_empty_words(self) -> None: + """Test with empty word list.""" + assert _fill_missing_word_bounds([], []) == [] + + def test_overlapping_bounds_clamped(self) -> None: + """Test that overlapping start times are clamped to previous end.""" + words = ["hello", "world"] + bounds: list[tuple[float, float] | None] = [(0.0, 0.7), (0.3, 1.0)] + result = _fill_missing_word_bounds(words, bounds) + + assert result[0].end == 0.7 + assert result[1].start == 0.7 # clamped to previous end + assert result[1].end == 1.0 + + +class TestDeterministicPipeline: + """End-to-end deterministic test for the CTC alignment pipeline. + + Constructs a synthetic emission matrix with clear peaks at known positions, + runs the full pipeline, and asserts word boundaries match expected positions. + """ + + def test_two_tokens_clear_peaks(self) -> None: + """Two tokens with clear peaks produce correct word boundaries. + + Emission matrix: 20 frames, 4 classes (blank=0, a=1, b=2, sep=3). + Token 'a' peaks at frames 3-6, separator '|' peaks at frames 8-10, + token 'b' peaks at frames 12-15. The separator peak is necessary + to force the CTC trellis to transition between tokens at the + right location (without it, blank emissions keep the path stuck + on the first token). + """ + num_frames = 20 + num_classes = 4 # blank, a, b, separator | + blank_id = 0 + + emission = torch.full((num_frames, num_classes), -10.0) + emission[:, blank_id] = -1.0 # blank moderate everywhere + + # Token a=1 peaks at frames 3-6 + for f in range(3, 7): + emission[f, 1] = -0.01 + + # Separator |=3 peaks at frames 8-10 (silence between words) + for f in range(8, 11): + emission[f, 3] = -0.01 + + # Token b=2 peaks at frames 12-15 + for f in range(12, 16): + emission[f, 2] = -0.01 + + dictionary = {"a": 1, "b": 2, "|": 3, "[pad]": 0} + words = ["a", "b"] + tokens, token_to_word = _build_alignment_tokens(words, dictionary) + assert tokens == [1, 3, 2] # a, |, b + + trellis = _get_trellis(emission, tokens, blank_id) + path = _backtrack(trellis, emission, tokens, blank_id) + assert len(path) == num_frames + + char_segments = _merge_repeats(path) + duration = 1.0 # 1 second of audio + ratio = duration / (trellis.shape[0] - 1) + + result = _segments_to_words(char_segments, token_to_word, words, ratio) + + assert len(result) == 2 + assert result[0].word == "a" + assert result[1].word == "b" + + # Both words should have non-negative duration + assert result[0].end >= result[0].start + assert result[1].end >= result[1].start + + # Words should not overlap and should be ordered + assert result[0].end <= result[1].start + + # Word "a" should cover at least the peak region (frames 3-6) + assert result[0].end >= 6 * ratio + + # Word "b" should start within or near its peak region (frames 12-15) + assert result[1].start <= 15 * ratio + + def test_three_words_with_wildcard(self) -> None: + """Three words including one with a wildcard character. + + Tests that the full pipeline handles wildcard tokens correctly and + produces reasonable boundaries for all words. + """ + num_frames = 12 + num_classes = 5 # blank=0, h=1, i=2, separator=3, x=4 + blank_id = 0 + + emission = torch.full((num_frames, num_classes), -10.0) + emission[:, blank_id] = -1.0 + + # "h" peaks at frames 1-2 + emission[1, 1] = -0.01 + emission[2, 1] = -0.01 + # "i" peaks at frames 4-5 + emission[4, 2] = -0.01 + emission[5, 2] = -0.01 + # "x" peaks at frames 8-9 + emission[8, 4] = -0.01 + emission[9, 4] = -0.01 + + dictionary = {"h": 1, "i": 2, "|": 3, "x": 4, "[pad]": 0} + # "h!" has wildcard for "!", "i" is clean, "x" is clean + words = ["h!", "i", "x"] + tokens, token_to_word = _build_alignment_tokens(words, dictionary) + # h=1, wildcard=-1 for "!", sep=3, i=2, sep=3, x=4 + assert tokens == [1, -1, 3, 2, 3, 4] + + trellis = _get_trellis(emission, tokens, blank_id) + path = _backtrack(trellis, emission, tokens, blank_id) + assert len(path) == num_frames + + char_segments = _merge_repeats(path) + ratio = 1.0 / (trellis.shape[0] - 1) + result = _segments_to_words(char_segments, token_to_word, words, ratio) + + assert len(result) == 3 + assert result[0].word == "h!" + assert result[1].word == "i" + assert result[2].word == "x" + # All words should have non-negative durations + for w in result: + assert w.end >= w.start + # Words should be ordered + assert result[0].end <= result[1].start + assert result[1].end <= result[2].start + + +class TestFallbackWordAlignment: + """Tests for _fallback_word_alignment function.""" + + def test_proportional_timing(self) -> None: + """Test that words get timestamps proportional to character length.""" + waveform = torch.zeros(1, 16000) # 1 second at 16kHz + words = ["hi", "there"] # 2 + 5 = 7 chars + + result = _fallback_word_alignment(words, waveform, 16000) + + assert len(result) == 2 + assert result[0].word == "hi" + assert result[1].word == "there" + # "hi" = 2/7 of 1s ≈ 0.286s, "there" = 5/7 ≈ 0.714s + assert result[0].start == pytest.approx(0.0) + assert result[0].end == pytest.approx(2 / 7) + assert result[1].start == pytest.approx(2 / 7) + assert result[1].end == pytest.approx(1.0) + + def test_single_word(self) -> None: + """Test that a single word spans the full duration.""" + waveform = torch.zeros(1, 32000) # 2 seconds + result = _fallback_word_alignment(["hello"], waveform, 16000) + + assert len(result) == 1 + assert result[0].start == pytest.approx(0.0) + assert result[0].end == pytest.approx(2.0) + + def test_empty_words(self) -> None: + """Test that empty word list returns empty.""" + waveform = torch.zeros(1, 16000) + assert _fallback_word_alignment([], waveform, 16000) == [] + + def test_zero_duration(self) -> None: + """Test that zero-length waveform gives zero timestamps.""" + waveform = torch.zeros(1, 0) + result = _fallback_word_alignment(["hello"], waveform, 16000) + + assert len(result) == 1 + assert result[0].start == 0.0 + assert result[0].end == 0.0 + + +class TestAlignPaddingBranch: + """Tests for the padding branch in align() for short audio.""" + + def test_short_audio_uses_original_duration(self, tmp_path: Path) -> None: + """Test that padding doesn't inflate timestamps. + + When audio is shorter than 400 samples, it gets padded for wav2vec2. + The duration computation must use the original (pre-padding) length + to avoid stretching timestamps. This matches WhisperX behavior where + duration = t2 - t1 (actual segment duration, not padded). + """ + audio_file = tmp_path / "test.wav" + audio_file.touch() + + original_samples = 200 # shorter than MIN_WAV2VEC2_SAMPLES (400) + mock_waveform = torch.zeros(1, original_samples) + # Model produces emissions from padded input (400 samples) + mock_emissions = torch.randn(1, 20, 29) + + mock_model = MagicMock() + mock_model.return_value = (mock_emissions, None) + mock_model.to = MagicMock(return_value=mock_model) + + mock_bundle = MagicMock() + mock_bundle.get_model.return_value = mock_model + mock_bundle.get_labels.return_value = list("abcdefghijklmnopqrstuvwxyz|' ") + + mock_pipelines = MagicMock() + mock_pipelines.__dict__ = {"WAV2VEC2_ASR_BASE_960H": mock_bundle} + + with ( + patch("torchaudio.load", return_value=(mock_waveform, 16000)), + patch("torchaudio.pipelines", mock_pipelines), + ): + words = align(audio_file, "hi", language="en") + + assert isinstance(words, list) + if words: + # All timestamps must be within the original audio duration + original_duration = original_samples / 16000 # 0.0125s + for word in words: + assert word.end <= original_duration + 0.001 diff --git a/tests/test_diarization.py b/tests/test_diarization.py new file mode 100644 index 00000000..921ee8db --- /dev/null +++ b/tests/test_diarization.py @@ -0,0 +1,674 @@ +"""Tests for the speaker diarization module.""" + +from __future__ import annotations + +import json +from typing import TYPE_CHECKING +from unittest.mock import MagicMock, patch + +import pytest + +from agent_cli.core.alignment import AlignedWord +from agent_cli.core.diarization import ( + DiarizedSegment, + _get_dominant_speaker_window, + _split_into_sentences, + align_transcript_with_speakers, + align_transcript_with_words, + align_words_to_speakers, + format_diarized_output, +) + +if TYPE_CHECKING: + from pathlib import Path + + +class TestDiarizedSegment: + """Tests for the DiarizedSegment dataclass.""" + + def test_create_segment(self): + """Test creating a diarized segment.""" + segment = DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.5, text="Hello") + assert segment.speaker == "SPEAKER_00" + assert segment.start == 0.0 + assert segment.end == 2.5 + assert segment.text == "Hello" + + def test_segment_default_text(self): + """Test that text defaults to empty string.""" + segment = DiarizedSegment(speaker="SPEAKER_01", start=1.0, end=3.0) + assert segment.text == "" + + +class TestAlignTranscriptWithSpeakers: + """Tests for the align_transcript_with_speakers function.""" + + def test_empty_segments(self): + """Test with empty segment list.""" + result = align_transcript_with_speakers("Hello world.", []) + assert result == [] + + def test_empty_transcript(self): + """Test with empty transcript.""" + segments = [DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0)] + result = align_transcript_with_speakers("", segments) + # Returns original segments when transcript is empty + assert result == segments + + def test_single_segment(self): + """Test alignment with a single segment.""" + segments = [DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=5.0)] + result = align_transcript_with_speakers("Hello world.", segments) + assert len(result) == 1 + assert result[0].text == "Hello world." + assert result[0].speaker == "SPEAKER_00" + + def test_two_sentences_two_speakers(self): + """Test that sentences are assigned to the correct speakers.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0), # 2s + DiarizedSegment(speaker="SPEAKER_01", start=2.0, end=4.0), # 2s + ] + # Two sentences of roughly equal length + transcript = "Hello, how are you? I am doing well." + result = align_transcript_with_speakers(transcript, segments) + assert len(result) == 2 + assert result[0].speaker == "SPEAKER_00" + assert result[0].text == "Hello, how are you?" + assert result[1].speaker == "SPEAKER_01" + assert result[1].text == "I am doing well." + + def test_three_sentences_three_speakers(self): + """Test sentences distribute across three speakers.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=1.0), # 1s + DiarizedSegment(speaker="SPEAKER_01", start=1.0, end=2.0), # 1s + DiarizedSegment(speaker="SPEAKER_02", start=2.0, end=3.0), # 1s + ] + # Three sentences of roughly equal length + transcript = "First sentence here. Second sentence here. Third sentence here." + result = align_transcript_with_speakers(transcript, segments) + assert len(result) == 3 + assert result[0].speaker == "SPEAKER_00" + assert result[1].speaker == "SPEAKER_01" + assert result[2].speaker == "SPEAKER_02" + + def test_consecutive_sentences_same_speaker_merged(self): + """Test that consecutive sentences from same speaker are merged.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=3.0), # 3s - one speaker + ] + transcript = "First sentence. Second sentence. Third sentence." + result = align_transcript_with_speakers(transcript, segments) + # All sentences should be merged into one segment + assert len(result) == 1 + assert result[0].speaker == "SPEAKER_00" + assert result[0].text == "First sentence. Second sentence. Third sentence." + + def test_zero_duration_fallback(self): + """Test fallback when total duration is zero.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=0.0), + DiarizedSegment(speaker="SPEAKER_01", start=0.0, end=0.0), + ] + transcript = "All text goes to first speaker." + result = align_transcript_with_speakers(transcript, segments) + # Zero duration fallback: all text to first speaker + assert len(result) == 1 + assert result[0].speaker == "SPEAKER_00" + assert result[0].text == transcript + + def test_single_sentence_no_punctuation(self): + """Test that text without punctuation is treated as single sentence.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0), + DiarizedSegment(speaker="SPEAKER_01", start=2.0, end=4.0), + ] + # No punctuation = single sentence = assigned to dominant speaker at start + transcript = "hello world how are you" + result = align_transcript_with_speakers(transcript, segments) + assert len(result) == 1 + assert result[0].text == transcript + + +class TestFormatDiarizedOutput: + """Tests for the format_diarized_output function.""" + + def test_inline_format(self): + """Test inline format output.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0, text="Hello"), + DiarizedSegment(speaker="SPEAKER_01", start=2.0, end=4.0, text="Hi there"), + ] + result = format_diarized_output(segments, output_format="inline") + expected = "[SPEAKER_00]: Hello\n[SPEAKER_01]: Hi there" + assert result == expected + + def test_inline_skips_empty_text(self): + """Test that inline format skips segments with empty text.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0, text="Hello"), + DiarizedSegment(speaker="SPEAKER_01", start=2.0, end=4.0, text=""), + DiarizedSegment(speaker="SPEAKER_00", start=4.0, end=6.0, text="Goodbye"), + ] + result = format_diarized_output(segments, output_format="inline") + expected = "[SPEAKER_00]: Hello\n[SPEAKER_00]: Goodbye" + assert result == expected + + def test_json_format(self): + """Test JSON format output.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.5, text="Hello"), + DiarizedSegment(speaker="SPEAKER_01", start=2.7, end=4.1, text="Hi there"), + ] + result = format_diarized_output(segments, output_format="json") + parsed = json.loads(result) + assert "segments" in parsed + assert len(parsed["segments"]) == 2 + assert parsed["segments"][0]["speaker"] == "SPEAKER_00" + assert parsed["segments"][0]["start"] == 0.0 + assert parsed["segments"][0]["end"] == 2.5 + assert parsed["segments"][0]["text"] == "Hello" + assert parsed["segments"][1]["speaker"] == "SPEAKER_01" + assert parsed["segments"][1]["start"] == 2.7 + assert parsed["segments"][1]["end"] == 4.1 + assert parsed["segments"][1]["text"] == "Hi there" + + def test_json_rounds_timestamps(self): + """Test that JSON format rounds timestamps to 2 decimal places.""" + segments = [ + DiarizedSegment( + speaker="SPEAKER_00", + start=0.123456, + end=2.987654, + text="Hello", + ), + ] + result = format_diarized_output(segments, output_format="json") + parsed = json.loads(result) + assert parsed["segments"][0]["start"] == 0.12 + assert parsed["segments"][0]["end"] == 2.99 + + def test_empty_segments(self): + """Test with empty segment list.""" + result_inline = format_diarized_output([], output_format="inline") + result_json = format_diarized_output([], output_format="json") + assert result_inline == "" + parsed = json.loads(result_json) + assert parsed["segments"] == [] + + +class TestCheckPyannoteInstalled: + """Tests for the pyannote installation check.""" + + def test_check_raises_when_not_installed(self): + """Test that ImportError is raised when pyannote is not installed.""" + from agent_cli.core.diarization import _check_pyannote_installed # noqa: PLC0415 + + with ( + patch.dict("sys.modules", {"pyannote.audio": None}), + patch( + "builtins.__import__", + side_effect=ImportError("No module named 'pyannote'"), + ), + pytest.raises(ImportError) as exc_info, + ): + _check_pyannote_installed() + assert "pyannote-audio is required" in str(exc_info.value) + assert "pip install agent-cli[diarization]" in str(exc_info.value) + + +class TestSpeakerDiarizer: + """Tests for the SpeakerDiarizer class.""" + + def test_diarizer_init_without_pyannote(self): + """Test that SpeakerDiarizer raises ImportError when pyannote not installed.""" + from agent_cli.core.diarization import SpeakerDiarizer # noqa: PLC0415 + + with ( + patch( + "agent_cli.core.diarization._check_pyannote_installed", + side_effect=ImportError("pyannote-audio is required"), + ), + pytest.raises(ImportError), + ): + SpeakerDiarizer(hf_token="test_token") # noqa: S106 + + def test_diarizer_init_with_mock_pyannote(self): + """Test SpeakerDiarizer initialization with mocked pyannote.""" + from agent_cli.core.diarization import SpeakerDiarizer # noqa: PLC0415 + + mock_pipeline = MagicMock() + mock_pipeline_class = MagicMock() + mock_pipeline_class.from_pretrained.return_value = mock_pipeline + + with ( + patch( + "agent_cli.core.diarization._check_pyannote_installed", + ), + patch.dict( + "sys.modules", + {"pyannote.audio": MagicMock(Pipeline=mock_pipeline_class)}, + ), + ): + diarizer = SpeakerDiarizer( + hf_token="test_token", # noqa: S106 + min_speakers=2, + max_speakers=4, + ) + assert diarizer.min_speakers == 2 + assert diarizer.max_speakers == 4 + mock_pipeline_class.from_pretrained.assert_called_once_with( + "pyannote/speaker-diarization-3.1", + token="test_token", # noqa: S106 + ) + + def test_diarizer_diarize(self, tmp_path: Path): + """Test diarization with mocked pipeline.""" + import torch # noqa: PLC0415 + + from agent_cli.core.diarization import SpeakerDiarizer # noqa: PLC0415 + + # Create a mock diarization result + mock_turn1 = MagicMock() + mock_turn1.start = 0.0 + mock_turn1.end = 2.5 + mock_turn2 = MagicMock() + mock_turn2.start = 2.5 + mock_turn2.end = 5.0 + + mock_annotation = MagicMock() + mock_annotation.itertracks.return_value = [ + (mock_turn1, None, "SPEAKER_00"), + (mock_turn2, None, "SPEAKER_01"), + ] + + # Mock DiarizeOutput (new API) + mock_output = MagicMock() + mock_output.speaker_diarization = mock_annotation + + mock_pipeline = MagicMock() + mock_pipeline.return_value = mock_output + + mock_pipeline_class = MagicMock() + mock_pipeline_class.from_pretrained.return_value = mock_pipeline + + # Mock torchaudio.load + mock_waveform = torch.zeros(1, 16000) + mock_sample_rate = 16000 + + with ( + patch("agent_cli.core.diarization._check_pyannote_installed"), + patch.dict( + "sys.modules", + {"pyannote.audio": MagicMock(Pipeline=mock_pipeline_class)}, + ), + patch("torchaudio.load", return_value=(mock_waveform, mock_sample_rate)), + ): + diarizer = SpeakerDiarizer(hf_token="test_token") # noqa: S106 + audio_file = tmp_path / "test.wav" + audio_file.touch() + + segments = diarizer.diarize(audio_file) + + assert len(segments) == 2 + assert segments[0].speaker == "SPEAKER_00" + assert segments[0].start == 0.0 + assert segments[0].end == 2.5 + assert segments[1].speaker == "SPEAKER_01" + assert segments[1].start == 2.5 + assert segments[1].end == 5.0 + # Pipeline should be called with audio dict, not file path + mock_pipeline.assert_called_once() + call_args = mock_pipeline.call_args[0][0] + assert "waveform" in call_args + assert "sample_rate" in call_args + + def test_diarizer_diarize_with_speaker_hints(self, tmp_path: Path): + """Test diarization passes speaker hints to pipeline.""" + import torch # noqa: PLC0415 + + from agent_cli.core.diarization import SpeakerDiarizer # noqa: PLC0415 + + mock_annotation = MagicMock() + mock_annotation.itertracks.return_value = [] + + # Mock DiarizeOutput (new API) + mock_output = MagicMock() + mock_output.speaker_diarization = mock_annotation + + mock_pipeline = MagicMock() + mock_pipeline.return_value = mock_output + + mock_pipeline_class = MagicMock() + mock_pipeline_class.from_pretrained.return_value = mock_pipeline + + # Mock torchaudio.load + mock_waveform = torch.zeros(1, 16000) + mock_sample_rate = 16000 + + with ( + patch("agent_cli.core.diarization._check_pyannote_installed"), + patch.dict( + "sys.modules", + {"pyannote.audio": MagicMock(Pipeline=mock_pipeline_class)}, + ), + patch("torchaudio.load", return_value=(mock_waveform, mock_sample_rate)), + ): + diarizer = SpeakerDiarizer( + hf_token="test_token", # noqa: S106 + min_speakers=2, + max_speakers=4, + ) + audio_file = tmp_path / "test.wav" + audio_file.touch() + + diarizer.diarize(audio_file) + + # Check speaker hints were passed + mock_pipeline.assert_called_once() + call_kwargs = mock_pipeline.call_args[1] + assert call_kwargs["min_speakers"] == 2 + assert call_kwargs["max_speakers"] == 4 + + +class TestGetDominantSpeakerWindow: + """Tests for the _get_dominant_speaker_window function.""" + + def test_single_segment_full_overlap(self): + """Test with single segment fully overlapping time range.""" + segments = [DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=5.0)] + speaker, _ = _get_dominant_speaker_window(1.0, 3.0, segments, 0) + assert speaker == "SPEAKER_00" + + def test_multiple_segments_picks_most_overlap(self): + """Test that speaker with most overlap wins.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0), # 1s overlap + DiarizedSegment(speaker="SPEAKER_01", start=1.0, end=4.0), # 2s overlap + ] + # Time range 1.0-3.0: SPEAKER_00 has 1s, SPEAKER_01 has 2s + speaker, _ = _get_dominant_speaker_window(1.0, 3.0, segments, 0) + assert speaker == "SPEAKER_01" + + def test_no_overlap_returns_none(self): + """Test that None is returned when no segments overlap.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=1.0), + DiarizedSegment(speaker="SPEAKER_01", start=5.0, end=6.0), + ] + speaker, _ = _get_dominant_speaker_window(2.0, 4.0, segments, 0) + assert speaker is None + + def test_empty_segments_returns_none(self): + """Test with empty segment list.""" + speaker, _ = _get_dominant_speaker_window(0.0, 1.0, [], 0) + assert speaker is None + + def test_same_speaker_multiple_segments(self): + """Test that durations from same speaker are summed.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=1.0), # 1s + DiarizedSegment(speaker="SPEAKER_01", start=1.0, end=2.0), # 1s + DiarizedSegment(speaker="SPEAKER_00", start=2.0, end=3.0), # 1s + ] + # SPEAKER_00 has 2s total, SPEAKER_01 has 1s + speaker, _ = _get_dominant_speaker_window(0.0, 3.0, segments, 0) + assert speaker == "SPEAKER_00" + + def test_cursor_advances_past_earlier_segments(self): + """Test that cursor skips segments ending before the query window.""" + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=1.0), + DiarizedSegment(speaker="SPEAKER_01", start=2.0, end=4.0), + ] + speaker, cursor = _get_dominant_speaker_window(2.5, 3.5, segments, 0) + assert speaker == "SPEAKER_01" + assert cursor == 1 + + +class TestAlignWordsToSpeakers: + """Tests for the align_words_to_speakers function.""" + + def test_empty_words(self): + """Test with empty word list.""" + segments = [DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=5.0)] + result = align_words_to_speakers([], segments) + assert result == segments + + def test_empty_segments(self): + """Test with empty segment list.""" + words = [AlignedWord(word="hello", start=0.0, end=1.0)] + result = align_words_to_speakers(words, []) + assert result == [] + + def test_single_word_single_speaker(self): + """Test single word assigned to single speaker.""" + words = [AlignedWord(word="hello", start=0.5, end=1.5)] + segments = [DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0)] + + result = align_words_to_speakers(words, segments) + + assert len(result) == 1 + assert result[0].speaker == "SPEAKER_00" + assert result[0].text == "hello" + assert result[0].start == 0.5 + assert result[0].end == 1.5 + + def test_words_assigned_to_correct_speakers(self): + """Test words are assigned based on overlap with speaker segments.""" + words = [ + AlignedWord(word="hello", start=0.0, end=1.0), + AlignedWord(word="there", start=1.0, end=2.0), + AlignedWord(word="friend", start=2.0, end=3.0), + ] + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=1.5), + DiarizedSegment(speaker="SPEAKER_01", start=1.5, end=3.0), + ] + + result = align_words_to_speakers(words, segments) + + assert len(result) == 2 + assert result[0].speaker == "SPEAKER_00" + assert "hello" in result[0].text + assert result[1].speaker == "SPEAKER_01" + + def test_consecutive_words_same_speaker_merged(self): + """Test that consecutive words from same speaker are merged.""" + words = [ + AlignedWord(word="hello", start=0.0, end=0.5), + AlignedWord(word="world", start=0.5, end=1.0), + ] + segments = [DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0)] + + result = align_words_to_speakers(words, segments) + + assert len(result) == 1 + assert result[0].text == "hello world" + assert result[0].start == 0.0 + assert result[0].end == 1.0 + + def test_word_without_overlap_uses_last_speaker(self): + """Test word without overlap uses previous speaker.""" + words = [ + AlignedWord(word="hello", start=0.0, end=1.0), + AlignedWord(word="gap", start=5.0, end=6.0), # No segment here + ] + segments = [DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0)] + + result = align_words_to_speakers(words, segments) + + # "gap" should use SPEAKER_00 (last known speaker) + assert len(result) == 1 + assert result[0].speaker == "SPEAKER_00" + assert "gap" in result[0].text + + +class TestAlignTranscriptWithWords: + """Tests for the align_transcript_with_words function.""" + + def test_empty_transcript(self): + """Test with empty transcript.""" + segments = [DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0)] + result = align_transcript_with_words("", segments, audio_path=None, language="en") + assert result == segments + + def test_empty_segments(self, tmp_path: Path): + """Test with empty segment list.""" + result = align_transcript_with_words( + "hello world", + [], + audio_path=tmp_path / "test.wav", + language="en", + ) + assert result == [] + + def test_calls_align_and_assigns_speakers(self, tmp_path: Path): + """Test that alignment is called and speakers are assigned.""" + audio_file = tmp_path / "test.wav" + audio_file.touch() + + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=1.0), + DiarizedSegment(speaker="SPEAKER_01", start=1.0, end=2.0), + ] + + mock_words = [ + AlignedWord(word="hello", start=0.0, end=0.5), + AlignedWord(word="world", start=1.0, end=1.5), + ] + + with patch("agent_cli.core.diarization.align", return_value=mock_words): + result = align_transcript_with_words( + "hello world", + segments, + audio_path=audio_file, + language="en", + ) + + assert len(result) == 2 + assert result[0].speaker == "SPEAKER_00" + assert result[0].text == "hello" + assert result[1].speaker == "SPEAKER_01" + assert result[1].text == "world" + + def test_passes_language_to_align(self, tmp_path: Path): + """Test that language is passed to align function.""" + audio_file = tmp_path / "test.wav" + audio_file.touch() + + segments = [DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0)] + + with patch("agent_cli.core.diarization.align") as mock_align: + mock_align.return_value = [AlignedWord(word="bonjour", start=0.0, end=1.0)] + + align_transcript_with_words( + "bonjour", + segments, + audio_path=audio_file, + language="fr", + ) + + mock_align.assert_called_once() + call_args = mock_align.call_args + assert call_args[0][2] == "fr" # language argument + + def test_falls_back_to_sentence_alignment(self, tmp_path: Path): + """Test that empty alignment result falls back to sentence-based assignment. + + When align() returns no words (e.g., backtrack failure, no alignable + characters), align_transcript_with_words should fall back to + align_transcript_with_speakers rather than returning raw segments. + """ + audio_file = tmp_path / "test.wav" + audio_file.touch() + + segments = [ + DiarizedSegment(speaker="SPEAKER_00", start=0.0, end=2.0), + DiarizedSegment(speaker="SPEAKER_01", start=2.0, end=4.0), + ] + + with patch("agent_cli.core.diarization.align", return_value=[]): + result = align_transcript_with_words( + "Hello there. How are you?", + segments, + audio_path=audio_file, + language="en", + ) + + # Should have used sentence-based fallback, not returned raw segments + assert any(seg.text for seg in result) + assert result[0].speaker == "SPEAKER_00" + + +class TestSplitIntoSentences: + """Tests for the _split_into_sentences function.""" + + def test_simple_sentences(self): + """Test splitting basic sentences ending with periods.""" + result = _split_into_sentences("Hello world. How are you.") + assert result == ["Hello world.", "How are you."] + + def test_question_marks(self): + """Test splitting on question marks.""" + result = _split_into_sentences("What is this? It is a test.") + assert result == ["What is this?", "It is a test."] + + def test_exclamation_marks(self): + """Test splitting on exclamation marks.""" + result = _split_into_sentences("Wow! That is great.") + assert result == ["Wow!", "That is great."] + + def test_abbreviations_not_split(self): + """Test that common abbreviations don't cause splits.""" + result = _split_into_sentences("Dr. Smith went home. He was tired.") + assert result == ["Dr. Smith went home.", "He was tired."] + + def test_multiple_abbreviations(self): + """Test multiple abbreviations in one sentence.""" + result = _split_into_sentences("Mr. and Mrs. Jones left. They went home.") + assert result == ["Mr. and Mrs. Jones left.", "They went home."] + + def test_initialism_not_split(self): + """Test that initialisms like U.S. don't cause splits.""" + result = _split_into_sentences("The U.S. is large. It has many states.") + assert result == ["The U.S. is large.", "It has many states."] + + def test_no_punctuation(self): + """Text without sentence-ending punctuation returns as single sentence.""" + result = _split_into_sentences("hello world how are you") + assert result == ["hello world how are you"] + + def test_empty_string(self): + """Test that empty string returns empty list.""" + assert _split_into_sentences("") == [] + + def test_whitespace_only(self): + """Test that whitespace-only string returns empty list.""" + assert _split_into_sentences(" ") == [] + + def test_single_sentence(self): + """Test a single sentence with period.""" + result = _split_into_sentences("Hello world.") + assert result == ["Hello world."] + + def test_quoted_sentence(self): + """Test sentence ending with closing quote after punctuation.""" + result = _split_into_sentences('He said "hello." She replied.') + assert result == ['He said "hello."', "She replied."] + + def test_eg_abbreviation(self): + """Test that e.g. is not treated as sentence boundary.""" + result = _split_into_sentences("Use tools e.g. hammers and nails. Then build.") + assert result == ["Use tools e.g. hammers and nails.", "Then build."] + + def test_single_initial(self): + """Test single-letter initial like 'J.' doesn't split.""" + result = _split_into_sentences("J. Smith arrived. He sat down.") + assert result == ["J. Smith arrived.", "He sat down."] + + def test_mixed_punctuation(self): + """Test mixing question marks, exclamation marks, and periods.""" + result = _split_into_sentences("Really? Yes! It works.") + assert result == ["Really?", "Yes!", "It works."] diff --git a/uv.lock b/uv.lock index 7cb9dc13..9e101d29 100644 --- a/uv.lock +++ b/uv.lock @@ -56,6 +56,9 @@ dev = [ { name = "versioningit" }, { name = "zensical" }, ] +diarization = [ + { name = "pyannote-audio" }, +] faster-whisper = [ { name = "fastapi", extra = ["standard"] }, { name = "faster-whisper" }, @@ -180,6 +183,7 @@ requires-dist = [ { name = "pre-commit", marker = "extra == 'dev'", specifier = ">=3.0.0" }, { name = "pre-commit-uv", marker = "extra == 'dev'", specifier = ">=4.1.4" }, { name = "psutil", marker = "sys_platform == 'win32'" }, + { name = "pyannote-audio", marker = "extra == 'diarization'", specifier = ">=3.3" }, { name = "pydantic" }, { name = "pydantic-ai-slim", extras = ["duckduckgo", "google", "openai", "vertexai"], marker = "extra == 'llm'", specifier = ">=0.1.1" }, { name = "pydantic-ai-slim", extras = ["google", "openai"], marker = "extra == 'memory'", specifier = ">=0.1.1" }, @@ -216,7 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