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import gradio as gr
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torch
import phonemizer
import librosa
import base64
def lark(audioAsB64):
with open("audio.wav", "wb") as preWaveform:
preWaveform.write(base64.b64encode())
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)
iface = gr.Interface(fn=lark, inputs="text", outputs="text")
iface.launch()
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