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import gradio as gr
from transformers import Wav2Vec2ForCTC, AutoProcessor
import torch
import librosa
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="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 mono WAV
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
processor.tokenizer.set_target_lang(language)
model.load_adapter(language)
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
languages = list(processor.tokenizer.vocab.keys())
iface = gr.Interface(fn=transcribe,
inputs=[
gr.Audio(source="microphone", type="filepath"),
gr.Audio(source="upload", type="filepath"),
gr.Dropdown(choices=languages, label="Language")
],
outputs=["textbox"]
)
iface.launch() |