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15c8971 | 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 | from pathlib import Path
from typing import Optional
import tempfile
import subprocess
import os
import torch
import librosa
from fastapi import FastAPI, UploadFile, File, Form
from fastapi.middleware.cors import CORSMiddleware
from transformers import WhisperProcessor, WhisperForConditionalGeneration
MODEL_NAME = os.getenv("MODEL_NAME", "openai/whisper-small")
app = FastAPI(
title="Voice Complaint Transcriber API",
description="Converts citizen grievance audio into text using Whisper.",
version="1.0.0",
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # later replace with your Vercel frontend URL
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class VoiceComplaintTranscriber:
def __init__(self, model_name: str = MODEL_NAME):
self.model_name = model_name
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.dtype = torch.float16 if self.device == "cuda" else torch.float32
self.processor = WhisperProcessor.from_pretrained(self.model_name)
self.model = WhisperForConditionalGeneration.from_pretrained(
self.model_name,
torch_dtype=self.dtype,
).to(self.device)
self.model.eval()
def convert_to_wav(self, input_path: Path) -> Path:
output_path = input_path.with_suffix(".wav")
subprocess.run(
[
"ffmpeg",
"-y",
"-i", str(input_path),
"-ar", "16000",
"-ac", "1",
str(output_path),
],
check=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
)
return output_path
def transcribe(self, audio_path: Path, language: Optional[str] = "hi") -> str:
if audio_path.suffix.lower() != ".wav":
audio_path = self.convert_to_wav(audio_path)
audio_array, _ = librosa.load(
str(audio_path),
sr=16000,
mono=True,
)
inputs = self.processor(
audio_array,
sampling_rate=16000,
return_tensors="pt",
)
input_features = inputs.input_features.to(
device=self.device,
dtype=self.dtype,
)
generate_kwargs = {
"inputs": input_features,
"max_new_tokens": 256,
}
if language and language != "auto":
generate_kwargs["forced_decoder_ids"] = self.processor.get_decoder_prompt_ids(
language=language,
task="transcribe",
)
with torch.no_grad():
predicted_ids = self.model.generate(**generate_kwargs)
text = self.processor.batch_decode(
predicted_ids,
skip_special_tokens=True,
)[0].strip()
return text
transcriber = VoiceComplaintTranscriber()
@app.get("/")
def home():
return {
"message": "Voice Complaint Transcriber API is running",
"model": MODEL_NAME,
"device": transcriber.device,
}
@app.get("/health")
def health():
return {
"status": "ok",
"model": MODEL_NAME,
"device": transcriber.device,
}
@app.post("/transcribe")
async def transcribe_audio(
file: UploadFile = File(...),
language: str = Form("hi"), # hi / en / auto
):
suffix = Path(file.filename).suffix or ".ogg"
with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_file:
temp_path = Path(temp_file.name)
temp_file.write(await file.read())
wav_path = temp_path.with_suffix(".wav")
try:
text = transcriber.transcribe(
audio_path=temp_path,
language=language,
)
return {
"transcribed_text": text,
"language": language,
"model": MODEL_NAME,
"method": "whisper_direct_fastapi",
}
finally:
if temp_path.exists():
temp_path.unlink()
if wav_path.exists():
wav_path.unlink() |