Spaces:
Sleeping
Sleeping
File size: 13,278 Bytes
c034a74 2bb03a8 ff704b5 d0ae17f c034a74 6f1334b c034a74 2bb03a8 ff704b5 c034a74 2bb03a8 3a240c4 c034a74 3a240c4 c034a74 2bb03a8 c034a74 2bb03a8 a318fb7 2bb03a8 a1a0caf 2bb03a8 3a240c4 2bb03a8 a318fb7 a1a0caf 2bb03a8 3a240c4 2bb03a8 3a240c4 2bb03a8 3a240c4 2bb03a8 3a240c4 2bb03a8 3a240c4 2bb03a8 3a240c4 2bb03a8 a87cf29 3a240c4 a87cf29 3a240c4 2bb03a8 a318fb7 2bb03a8 908288f e3f5ff0 2bb03a8 ff704b5 2bb03a8 c034a74 2bb03a8 e3f5ff0 2bb03a8 c034a74 2bb03a8 fce7c66 2bb03a8 fce7c66 3a240c4 2bb03a8 3a240c4 2bb03a8 3a240c4 2bb03a8 3a240c4 2bb03a8 d0ae17f 2bb03a8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 |
# from fastapi import FastAPI, Response
# from fastapi.responses import FileResponse
# from kokoro import KPipeline
# import soundfile as sf
# import os
# import numpy as np
# import torch
# from huggingface_hub import InferenceClient
# def llm_chat_response(text):
# HF_TOKEN = os.getenv("HF_TOKEN")
# client = InferenceClient(api_key=HF_TOKEN)
# messages = [
# {
# "role": "user",
# "content": [
# {
# "type": "text",
# "text": text + str('describe in one line only')
# } #,
# # {
# # "type": "image_url",
# # "image_url": {
# # "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
# # }
# # }
# ]
# }
# ]
# response_from_llama = client.chat.completions.create(
# model="meta-llama/Llama-3.2-11B-Vision-Instruct",
# messages=messages,
# max_tokens=500)
# return response_from_llama.choices[0].message['content']
# app = FastAPI()
# # Initialize pipeline once at startup
# pipeline = KPipeline(lang_code='a')
# @app.post("/generate")
# async def generate_audio(text: str, voice: str = "af_heart", speed: float = 1.0):
# text_reply = llm_chat_response(text)
# # Generate audio
# generator = pipeline(
# text_reply,
# voice=voice,
# speed=speed,
# split_pattern=r'\n+'
# )
# # # Save first segment only for demo
# # for i, (gs, ps, audio) in enumerate(generator):
# # sf.write(f"output_{i}.wav", audio, 24000)
# # return FileResponse(
# # f"output_{i}.wav",
# # media_type="audio/wav",
# # filename="output.wav"
# # )
# # return Response("No audio generated", status_code=400)
# # Process only the first segment for demo
# for i, (gs, ps, audio) in enumerate(generator):
# # Convert PyTorch tensor to NumPy array
# audio_numpy = audio.cpu().numpy()
# # Convert to 16-bit PCM
# # Ensure the audio is in the range [-1, 1]
# audio_numpy = np.clip(audio_numpy, -1, 1)
# # Convert to 16-bit signed integers
# pcm_data = (audio_numpy * 32767).astype(np.int16)
# # Convert to bytes (automatically uses row-major order)
# raw_audio = pcm_data.tobytes()
# # Return PCM data with minimal necessary headers
# return Response(
# content=raw_audio,
# media_type="application/octet-stream",
# headers={
# "Content-Disposition": f'attachment; filename="output.pcm"',
# "X-Sample-Rate": "24000",
# "X-Bits-Per-Sample": "16",
# "X-Endianness": "little"
# }
# )
# return Response("No audio generated", status_code=400)
from fastapi import FastAPI, Response, HTTPException
from fastapi.responses import FileResponse, JSONResponse
from kokoro import KPipeline
import soundfile as sf
import os
import numpy as np
import torch
from huggingface_hub import InferenceClient
from pydantic import BaseModel
import base64
from io import BytesIO
from PIL import Image
import logging
from typing import Optional
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class TextImageRequest(BaseModel):
text: Optional[str] = None
image_base64: Optional[str] = None
voice: str = "af_heart" # Default voice that we know exists
speed: float = 1.0
# List of known available voices - update this based on what's actually available
AVAILABLE_VOICES = ["af_heart"] # Add more voices as they become available
# Validate that the voice exists
def validate_voice(self):
if self.voice not in self.AVAILABLE_VOICES:
return "af_heart" # Default to a voice we know exists
return self.voice
class AudioResponse(BaseModel):
status: str
message: str
class ErrorResponse(BaseModel):
error: str
detail: Optional[str] = None
# Initialize FastAPI app
app = FastAPI(
title="Text-to-Speech API with Vision Support",
description="API for generating speech from text with optional image analysis",
version="1.0.0"
)
def llm_chat_response(text, image_base64=None):
"""Function to get responses from LLM with text and optionally image input."""
try:
HF_TOKEN = os.getenv("HF_TOKEN")
logger.info("Checking HF_TOKEN...")
if not HF_TOKEN:
logger.error("HF_TOKEN not found in environment variables")
raise HTTPException(status_code=500, detail="HF_TOKEN not configured")
logger.info("Initializing InferenceClient...")
client = InferenceClient(
provider="together", # Using the provider shown in the sample
api_key=HF_TOKEN
)
try:
# IMPORTANT: Following exactly the format from the sample code
if image_base64:
logger.info("Processing request with image")
prompt = text if text else "Describe this image in one sentence."
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{image_base64}"
}
}
]
}
]
else:
logger.info("Processing text-only request")
messages = [
{
"role": "user",
"content": text + " Describe in one line only."
}
]
logger.info("Sending request to model...")
# Log the exact message structure we're sending
logger.info(f"Message structure: {messages}")
# Use the exact model name and parameters from the sample
completion = client.chat.completions.create(
model="meta-llama/Llama-3.2-11B-Vision-Instruct",
messages=messages,
max_tokens=500
)
logger.info(f"Received response from model")
# Very simple response handling exactly like the sample code
logger.info(f"Model response received: {completion}")
try:
# Extract response using the exact approach from the sample code
response = completion.choices[0].message.content
logger.info(f"Extracted response content: {response}")
return response
except Exception as e:
logger.error(f"Error extracting message content: {str(e)}")
logger.error(f"Attempting alternative extraction method...")
# Fallback method if the above fails
try:
if hasattr(completion.choices[0], "message"):
if hasattr(completion.choices[0].message, "content"):
return completion.choices[0].message.content
# Last resort - try accessing as dictionary
return completion.choices[0]["message"]["content"]
except Exception as e2:
logger.error(f"All extraction methods failed: {str(e2)}")
return "I couldn't process that input. Please try again with a different query."
except Exception as e:
logger.error(f"Error during model inference: {str(e)}")
# Fallback response in case of error
return "I couldn't process that input. Please try again with a different image or text query."
except Exception as e:
logger.error(f"Error in llm_chat_response: {str(e)}")
raise HTTPException(status_code=500, detail=str(e))
# Initialize pipeline once at startup
try:
logger.info("Initializing KPipeline...")
pipeline = KPipeline(lang_code='a')
logger.info("KPipeline initialized successfully")
except Exception as e:
logger.error(f"Failed to initialize KPipeline: {str(e)}")
# We'll let the app start anyway, but log the error
@app.post("/generate", response_model=None, responses={
200: {"content": {"application/octet-stream": {}}},
400: {"model": ErrorResponse},
500: {"model": ErrorResponse}
})
async def generate_audio(request: TextImageRequest):
"""
Generate audio from text and optionally analyze an image.
- If text is provided, uses that as input
- If image is provided, analyzes the image
- Converts the LLM response to speech using the specified voice and speed
"""
try:
logger.info(f"Received audio generation request")
# If no text is provided but image is provided, use default prompt
user_text = request.text if request.text is not None else ""
if not user_text and request.image_base64:
user_text = "Describe what you see in the image"
elif not user_text and not request.image_base64:
logger.error("Neither text nor image provided in request")
return JSONResponse(
status_code=400,
content={"error": "Request must include either text or image_base64"}
)
# Generate response using text and image if provided
logger.info("Getting LLM response...")
text_reply = llm_chat_response(user_text, request.image_base64)
logger.info(f"LLM response: {text_reply}")
# Validate voice parameter
validated_voice = request.validate_voice()
if validated_voice != request.voice:
logger.warning(f"Requested voice '{request.voice}' not available, using '{validated_voice}' instead")
# Generate audio
logger.info(f"Generating audio using voice={validated_voice}, speed={request.speed}")
try:
generator = pipeline(
text_reply,
voice=validated_voice,
speed=request.speed,
split_pattern=r'\n+'
)
# Process only the first segment for demo
for i, (gs, ps, audio) in enumerate(generator):
logger.info(f"Audio generated successfully: segment {i}")
# Convert PyTorch tensor to NumPy array
audio_numpy = audio.cpu().numpy()
# Convert to 16-bit PCM
# Ensure the audio is in the range [-1, 1]
audio_numpy = np.clip(audio_numpy, -1, 1)
# Convert to 16-bit signed integers
pcm_data = (audio_numpy * 32767).astype(np.int16)
# Convert to bytes (automatically uses row-major order)
raw_audio = pcm_data.tobytes()
# Return PCM data with minimal necessary headers
return Response(
content=raw_audio,
media_type="application/octet-stream",
headers={
"Content-Disposition": f'attachment; filename="output.pcm"',
"X-Sample-Rate": "24000",
"X-Bits-Per-Sample": "16",
"X-Endianness": "little"
}
)
logger.error("No audio segments generated")
return JSONResponse(
status_code=400,
content={"error": "No audio generated", "detail": "The pipeline did not produce any audio"}
)
except Exception as e:
logger.error(f"Error generating audio: {str(e)}")
return JSONResponse(
status_code=500,
content={"error": "Audio generation failed", "detail": str(e)}
)
except Exception as e:
logger.error(f"Unexpected error in generate_audio endpoint: {str(e)}")
return JSONResponse(
status_code=500,
content={"error": "Internal server error", "detail": str(e)}
)
@app.get("/")
async def root():
return {"message": "Welcome to the Text-to-Speech API with Vision Support. Use POST /generate endpoint with 'text' and optionally 'image_base64' for queries."}
@app.exception_handler(404)
async def not_found_handler(request, exc):
return JSONResponse(
status_code=404,
content={"error": "Endpoint not found. Please use POST /generate for queries."}
)
@app.exception_handler(405)
async def method_not_allowed_handler(request, exc):
return JSONResponse(
status_code=405,
content={"error": "Method not allowed. Please check the API documentation."}
) |