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import gradio as gr
from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
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)"):
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"
# Make sure audio is 16kHz
speech, sample_rate = librosa.load(audio_file)
if sample_rate != 16000:
speech = librosa.resample(speech, orig_sr=sample_rate, target_sr=16000)
# Keep the same model in memory and simply switch out the language adapters by calling load_adapter() for the model and set_target_lang() for the tokenizer
language_code = iso_codes[language]
processor.tokenizer.set_target_lang(language_code)
model.load_adapter(language_code)
inputs = processor(speech, 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)
return transcription
examples = [["kab_1.mp3", None, "Amazigh (kab)"],
["kab_2.mp3", None, "Amazigh (kab)"]]
description = '''Automatic Speech Recognition with [MMS](https://ai.facebook.com/blog/multilingual-model-speech-recognition/) (Massively Multilingual Speech) by Meta.
Supports [1162 languages](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html). Read the paper for more details: [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516).'''
iface = gr.Interface(fn=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
)
iface.launch() |