app.py

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()