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import gradio as gr
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torch
import phonemizer
import librosa
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
waveform, sample_rate = librosa.load('harvard.wav', sr=16000) # Downsample 44.1kHz to 8kHz
input_values = processor(waveform, sampling_rate=sample_rate, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
def showTranscription(transcription):
return transcription
iface = gr.Interface(fn=showTranscription, inputs="text", outputs="text")
iface.launch() |