app.py

import gradio as gr
import tensorflow as tf
from wav2vec2 import Wav2Vec2Processor, Wav2Vec2ForCTC


if __name__ == '__main__':
    processor = Wav2Vec2Processor(is_tokenizer=False)
    tokenizer = Wav2Vec2Processor(is_tokenizer=True)
    model = Wav2Vec2ForCTC.from_pretrained("vasudevgupta/gsoc-wav2vec2-960h")

    def _forward(speech: tf.Tensor):
        speech = processor(speech)[None]
        tf_out = model(speech, training=False)
        return tf.squeeze(tf.argmax(tf_out, axis=-1))

    def transcribe_text(inputs):
        _, speech = inputs
        speech = tf.constant(speech, dtype=tf.float32)
        speech = tf.transpose(speech)
        tf_out = _forward(speech)
        return tokenizer.decode(tf_out.numpy().tolist())

    gr.Interface(fn=transcribe_text, inputs="audio", outputs="text").launch()