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#uvicorn app:app --host 0.0.0.0 --port 8000 --reload | |
from fastapi import FastAPI, UploadFile, File | |
from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
from fastapi.responses import HTMLResponse | |
import librosa | |
import io | |
import re | |
html_tag_remover = re.compile(r'<[^>]+>') | |
def remove_tags(text): | |
return html_tag_remover.sub('', text) | |
app = FastAPI() | |
processor = WhisperProcessor.from_pretrained("openai/whisper-medium") | |
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium") | |
model.config.forced_decoder_ids = None | |
chunk_duration = 30 | |
overlap_duration = 5 | |
def read_root(): | |
html_form = """ | |
<html> | |
<body> | |
<h2>ASR Transcription</h2> | |
<form action="/transcribe" method="post" enctype="multipart/form-data"> | |
<label for="audio_file">Upload an audio file (MP3 or WAV):</label> | |
<input type="file" id="audio_file" name="audio_file" accept=".mp3, .wav" required><br><br> | |
<input type="submit" value="Transcribe"> | |
</form> | |
</body> | |
</html> | |
""" | |
return HTMLResponse(content=html_form, status_code=200) | |
async def transcribe_audio(audio_file: UploadFile): | |
audio_data = await audio_file.read() | |
audio_data, _ = librosa.load(io.BytesIO(audio_data), sr=16000) | |
transcription = [] | |
start = 0 | |
while start < len(audio_data): | |
end = start + chunk_duration * 16000 | |
audio_chunk = audio_data[start:end] | |
input_features = processor(audio_chunk.tolist(), return_tensors="pt").input_features | |
predicted_ids = model.generate(input_features, max_length=1000) | |
chunk_transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) | |
transcription.extend(chunk_transcription) | |
start = end - overlap_duration * 16000 | |
final_transcription = " ".join(transcription) | |
final_transcription = remove_tags(final_transcription) | |
return final_transcription |