app.py

import gradio as gr
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import soundfile as sf
import torch

# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h")
model = Wav2Vec2ForCTC.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h")

# define function to read in sound file
def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch

# tokenize

def recognize_speech(audio):
    ds = map_to_array({
        "file": audio
    })
    input_values = processor(ds["speech"], return_tensors="pt", padding="longest").input_values  # Batch size 1
    logits = model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = processor.batch_decode(predicted_ids)
    return transcription[0]

# launch a simple UI
gr.Interface(fn=recognize_speech, inputs="microphone", outputs="text").launch()