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from transformers import AutoModelForCTC, Wav2Vec2Tokenizer | |
import torch | |
import gradio as gr | |
model = Wav2Vec2ForCTC.from_pretrained("BenDaouda/wav2vec2-large-xls-r-300m-wolof-test-coloab") | |
processor = Wav2Vec2Processor.from_pretrained("BenDaouda/wav2vec2-large-xls-r-300m-wolof-test-coloab") | |
def transcribe(audio): | |
input_values = tokenizer(audio, return_tensors="pt").input_values | |
with torch.no_grad(): | |
logits = model(input_values).logits | |
predicted_ids = torch.argmax(logits, dim=-1) | |
transcription = tokenizer.batch_decode(predicted_ids)[0] | |
return transcription | |
iface = gr.Interface( | |
fn=transcribe, | |
inputs=gr.inputs.Audio(source="microphone", type="file", resample_to=16000), | |
outputs="text" | |
) | |
iface.launch() | |