Audio Sentiment Analysis
Demonstrates team-based transcription and sentiment analysis for audio conversations.
"""
Audio Sentiment Analysis
========================
Demonstrates team-based transcription and sentiment analysis for audio conversations.
"""
import requests
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.media import Audio
from agno.models.google import Gemini
from agno.team import Team
# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
transcription_agent = Agent(
name="Audio Transcriber",
role="Transcribe audio conversations accurately",
model=Gemini(id="gemini-3.5-flash"),
instructions=[
"Transcribe audio with speaker identification",
"Maintain conversation structure and flow",
],
)
sentiment_analyst = Agent(
name="Sentiment Analyst",
role="Analyze emotional tone and sentiment in conversations",
model=Gemini(id="gemini-3.5-flash"),
instructions=[
"Analyze sentiment for each speaker separately",
"Identify emotional patterns and conversation dynamics",
"Provide detailed sentiment insights",
],
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
sentiment_team = Team(
name="Audio Sentiment Team",
members=[transcription_agent, sentiment_analyst],
model=Gemini(id="gemini-3.5-flash"),
instructions=[
"Analyze audio sentiment with conversation memory.",
"Audio Transcriber: First transcribe audio with speaker identification.",
"Sentiment Analyst: Analyze emotional tone and conversation dynamics.",
],
add_history_to_context=True,
markdown=True,
db=SqliteDb(
session_table="audio_sentiment_team_sessions",
db_file="tmp/audio_sentiment_team.db",
),
)
# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
url = "https://agno-public.s3.amazonaws.com/demo_data/sample_conversation.wav"
response = requests.get(url)
audio_content = response.content
sentiment_team.print_response(
"Give a sentiment analysis of this audio conversation. Use speaker A, speaker B to identify speakers.",
audio=[Audio(content=audio_content)],
stream=True,
)
sentiment_team.print_response(
"What else can you tell me about this audio conversation?",
stream=True,
)Before running
The downloaded sample is WAV. Replace Audio(content=audio_content) with Audio(content=audio_content, mime_type="audio/wav"); Gemini otherwise labels untyped audio bytes with its MP3 default. Add response.raise_for_status() after downloading so an error body is not passed as audio. The second call uses the same in-process session; use an explicit session_id to continue it after restarting the script. Speaker labels and sentiment are model interpretations.
Run the Example
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activateInstall dependencies
uv pip install -U agno google-genai requests sqlalchemyExport your Google API key
export GOOGLE_API_KEY="your_google_api_key_here"Run the example
Save the code above as audio_sentiment_analysis.py, then run:
python audio_sentiment_analysis.pyFull source: cookbook/03_teams/19_multimodal/audio_sentiment_analysis.py