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import os
os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf-cache" # Important for Docker
from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
import torchaudio
import torch
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import io
app = FastAPI()
# Allow all origins (for Flutter)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
# Load model
processor = Wav2Vec2Processor.from_pretrained("Mustafaa4a/ASR-Somali")
model = Wav2Vec2ForCTC.from_pretrained("Mustafaa4a/ASR-Somali")
@app.get("/")
async def root():
return {"message": "Somali Speech-to-Text API is running."}
@app.post("/transcribe")
async def transcribe(file: UploadFile = File(...)):
audio_bytes = await file.read()
audio_stream = io.BytesIO(audio_bytes)
waveform, sample_rate = torchaudio.load(audio_stream)
if sample_rate != 16000:
resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
waveform = resampler(waveform)
inputs = processor(waveform.squeeze(), sampling_rate=16000, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0])
return {"transcription": transcription}
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