app.py

import gradio as gr
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torch
import phonemizer
import librosa
import io
import base64


def lark(audioAsB64):
    # base64 to wav data conversion
    wav_data = base64.b64decode(audioAsB64.encode("utf-8"))

    # processing
    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(
        io.BytesIO(wav_data), 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)

    return transcription


iface = gr.Interface(fn=lark, inputs="text", outputs="text")
iface.launch()