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
transcription_agent = Agent(
name="Audio Transcriber",
role="Transcribe audio conversations accurately",
model=Gemini(id="gemini-2.0-flash-exp"),
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-2.0-flash-exp"),
instructions=[
"Analyze sentiment for each speaker separately",
"Identify emotional patterns and conversation dynamics",
"Provide detailed sentiment insights",
],
)
# Create a team for collaborative audio sentiment analysis
sentiment_team = Team(
name="Audio Sentiment Team",
members=[transcription_agent, sentiment_analyst],
model=Gemini(id="gemini-2.0-flash-exp"),
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",
),
)
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,
)
