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import gradio as gr
from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
import numpy as np
import librosa
import json
with open('ISO_codes.json', 'r') as file:
iso_codes = json.load(file)
languages = list(iso_codes.keys())
model_id = "facebook/mms-1b-all"
processor = AutoProcessor.from_pretrained(model_id)
model = Wav2Vec2ForCTC.from_pretrained(model_id)
def transcribe(audio_file_mic=None, audio_file_upload=None, language="English (eng)", progress=gr.Progress()):
if audio_file_mic:
audio_file = audio_file_mic
elif audio_file_upload:
audio_file = audio_file_upload
else:
return "Please upload an audio file or record one"
progress(0, desc="Starting")
# Make sure audio is 16kHz
speech, sample_rate = librosa.load(audio_file)
if sample_rate != 16000:
progress(1, desc="Resampling")
speech = librosa.resample(speech, orig_sr=sample_rate, target_sr=16000)
# Cut speech into chunks
chunk_size = 30 * 16000 # 30s * 16000Hz
chunks = np.split(speech, np.arange(chunk_size, len(speech), chunk_size))
# load model adapter for this language
language_code = iso_codes[language]
processor.tokenizer.set_target_lang(language_code)
model.load_adapter(language_code)
transcriptions = []
progress(2, desc="Transcribing")
for chunk in progress.tqdm(chunks, desc="Transcribing"):
inputs = processor(chunk, sampling_rate=16_000, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs).logits
ids = torch.argmax(outputs, dim=-1)[0]
transcription = processor.decode(ids)
transcriptions.append(transcription)
transcription = ' '.join(transcriptions)
return transcription
examples = [
["balinese.mp3", None, "Bali (ban)"],
["madura.mp3", None, "Madura (mad)"],
["toba_batak.mp3", None, "Batak Toba (bbc)"],
["minangkabau.mp3", None, "Minangkabau (min)"],
]
description = '''Automatic Speech Recognition with [MMS](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) (Massively Multilingual Speech) by Meta.'''
demo = gr.Interface(
transcribe,
inputs=[
gr.Audio(source="microphone", type="filepath", label="Record Audio"),
gr.Audio(source="upload", type="filepath", label="Upload Audio"),
gr.Dropdown(choices=languages, label="Language", value="English (eng)")
],
outputs=gr.Textbox(label="Transcription"),
# examples=examples,
description=description
)
if __name__ == "__main__":
demo.queue(concurrency_count=1).launch()
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