chaty / app.py
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from fastrtc import (
ReplyOnPause, AdditionalOutputs, Stream,
audio_to_bytes, aggregate_bytes_to_16bit
)
import gradio as gr
from groq import Groq
import numpy as np
import anthropic
from elevenlabs import ElevenLabs
groq_client = Groq()
claude_client = anthropic.Anthropic()
tts_client = ElevenLabs()
# See "Talk to Claude" in Cookbook for an example of how to keep
# track of the chat history.
def response(
audio: tuple[int, np.ndarray],
):
prompt = groq_client.audio.transcriptions.create(
file=("audio-file.mp3", audio_to_bytes(audio)),
model="whisper-large-v3-turbo",
response_format="verbose_json",
).text
response = claude_client.messages.create(
model="claude-3-5-haiku-20241022",
max_tokens=512,
messages=[{"role": "user", "content": prompt}],
)
response_text = " ".join(
block.text
for block in response.content
if getattr(block, "type", None) == "text"
)
iterator = tts_client.text_to_speech.convert_as_stream(
text=response_text,
voice_id="JBFqnCBsd6RMkjVDRZzb",
model_id="eleven_multilingual_v2",
output_format="pcm_24000"
)
for chunk in aggregate_bytes_to_16bit(iterator):
audio_array = np.frombuffer(chunk, dtype=np.int16).reshape(1, -1)
yield (24000, audio_array)
stream = Stream(
modality="audio",
mode="send-receive",
handler=ReplyOnPause(response),
)