feat(python): audio-intelligence v1 — sentiment

recipes/python/audio-intelligence/v1/sentiment/README.md

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+# Sentiment Analysis (Audio Intelligence v1)
+
+Segment-level positive/negative/neutral sentiment scoring on audio content.
+
+## What it does
+
+When `sentiment=True` is set, Deepgram analyses the emotional tone of each segment in the transcript and labels it as positive, negative, or neutral. This is valuable for call-centre analytics, customer feedback processing, or understanding the emotional arc of conversations and recordings.
+
+## Key parameters
+
+| Parameter | Value | Description |
+|-----------|-------|-------------|
+| `sentiment` | `True` | Enable sentiment analysis |
+| `model` | `"nova-3"` | Transcription model |
+| `smart_format` | `True` | Format numbers, dates, etc. |
+
+## Example output
+
+```
+Transcript: Yeah, as much as it's worth celebrating the 50th anniversary of the spacewalk...
+
+Sentiment segments: 4
+  [positive] Yeah, as much as it's worth celebrating the 50th anniversary
+  [neutral] it's also worth noting that we've come a long way since then
+```
+
+## Prerequisites
+
+- Python 3.10+
+- Set `DEEPGRAM_API_KEY` environment variable
+- Install: `pip install -r recipes/python/requirements.txt`
+
+## Run
+
+```bash
+python example.py
+```
+
+## Test
+
+```bash
+pytest example_test.py -v
+```

recipes/python/audio-intelligence/v1/sentiment/example.py

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+"""
+Recipe: Sentiment Analysis (Audio Intelligence v1)
+====================================================
+Demonstrates segment-level sentiment analysis on audio content.
+
+When sentiment=True is set, each segment of the transcript is scored as
+positive, negative, or neutral with a confidence value. This is useful
+for call-centre analytics, customer feedback analysis, or understanding
+the emotional tone of conversations.
+"""
+
+from deepgram import DeepgramClient
+
+AUDIO_URL = "https://dpgr.am/spacewalk.wav"
+
+
+def main():
+    client = DeepgramClient()  # reads DEEPGRAM_API_KEY from environment
+
+    response = client.listen.v1.media.transcribe_url(
+        url=AUDIO_URL,
+        model="nova-3",
+        smart_format=True,
+        sentiment=True,  # <-- THIS enables sentiment analysis.
+    )
+
+    if response.results and response.results.channels:
+        transcript = response.results.channels[0].alternatives[0].transcript
+        print(f"Transcript: {transcript[:150]}...")
+
+    # Sentiment results appear in response.results.sentiments.segments
+    if hasattr(response.results, "sentiments") and response.results.sentiments:
+        segments = getattr(response.results.sentiments, "segments", [])
+        print(f"\nSentiment segments: {len(segments)}")
+        for seg in segments[:5]:
+            sentiment = getattr(seg, "sentiment", "unknown")
+            text = getattr(seg, "text", "")
+            print(f"  [{sentiment}] {text[:80]}")
+
+
+if __name__ == "__main__":
+    main()

recipes/python/audio-intelligence/v1/sentiment/example_test.py

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+import subprocess
+from pathlib import Path
+
+def test_example_runs():
+    """Runs the sentiment example and verifies it produces output."""
+    example = Path(__file__).parent / "example.py"
+    result = subprocess.run(
+        ["python", str(example)],
+        capture_output=True,
+        text=True,
+        timeout=60,
+    )
+    assert result.returncode == 0, (
+        f"Example failed\nSTDOUT: {result.stdout}\nSTDERR: {result.stderr}"
+    )
+    assert result.stdout.strip(), "Example produced no output"
+    assert len(result.stdout.strip()) > 10, "Output seems too short"