File size: 3,403 Bytes
3b4968e 7db8704 3b4968e 5dda573 3b4968e 7db8704 3b4968e 7db8704 3b4968e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
from fastapi import FastAPI, UploadFile, File, Response, Request
from fastapi.staticfiles import StaticFiles
import ggwave
import scipy.io.wavfile as wav
import numpy as np
import os
from pydantic import BaseModel
from groq import Groq
import io
import wave
app = FastAPI()
# Serve static files
app.mount("/static", StaticFiles(directory="static"), name="static")
# Initialize ggwave instance
instance = ggwave.init()
# Initialize Groq client
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
class TextInput(BaseModel):
text: str
@app.get("/")
async def serve_homepage():
"""Serve the chat interface HTML."""
with open("static/index.html", "r") as f:
return Response(content=f.read(), media_type="text/html")
@app.post("/stt/")
async def speech_to_text(file: UploadFile = File(...)):
"""Convert WAV audio file to text using ggwave."""
with open("temp.wav", "wb") as audio_file:
audio_file.write(await file.read())
# Load WAV file
fs, recorded_waveform = wav.read("temp.wav")
os.remove("temp.wav")
# Convert to bytes and decode
waveform_bytes = recorded_waveform.astype(np.uint8).tobytes()
decoded_message = ggwave.decode(instance, waveform_bytes)
return {"text": decoded_message}
@app.post("/tts/")
def text_to_speech(input_text: TextInput):
"""Convert text to a WAV audio file using ggwave and return as response."""
encoded_waveform = ggwave.encode(instance, input_text.text.encode('utf-8'), protocolId=1, volume=100)
# Convert byte data into float32 array
waveform_float32 = np.frombuffer(encoded_waveform, dtype=np.float32)
# Normalize float32 data to the range of int16
waveform_int16 = np.int16(waveform_float32 * 32767)
# Save to buffer instead of a file
buffer = io.BytesIO()
with wave.open(buffer, "wb") as wf:
wf.setnchannels(1) # Mono audio
wf.setsampwidth(2) # 2 bytes per sample (16-bit PCM)
wf.setframerate(48000) # Sample rate
wf.writeframes(waveform_int16.tobytes()) # Write waveform as bytes
buffer.seek(0)
return Response(content=buffer.getvalue(), media_type="audio/wav")
@app.post("/chat/")
async def chat_with_llm(file: UploadFile = File(...)):
"""Process input WAV, send text to LLM, and return generated response as WAV."""
with open("input_chat.wav", "wb") as audio_file:
audio_file.write(await file.read())
# Load WAV file
fs, recorded_waveform = wav.read("input_chat.wav")
os.remove("input_chat.wav")
# Convert to bytes and decode
waveform_bytes = recorded_waveform.astype(np.uint8).tobytes()
user_message = ggwave.decode(instance, waveform_bytes)
# Send to LLM
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": user_message}],
model="llama-3.3-70b-versatile",
)
llm_response = chat_completion.choices[0].message.content
# Convert response to audio
response_waveform = ggwave.encode(instance, llm_response)
buffer = io.BytesIO()
wav.write(buffer, 44100, np.frombuffer(response_waveform, dtype=np.uint8))
buffer.seek(0)
return Response(content=buffer.getvalue(), media_type="audio/wav", headers={
"X-User-Message": user_message,
"X-LLM-Response": llm_response
}) |