app.py

from typing import Tuple

import numpy as np

import torch
from torchaudio.transforms import Resample

from huggingface_hub import hf_hub_download

import gradio as gr

from pipeline import PreTrainedPipeline


HF_HUB_URL = 'ales/wav2vec2-cv-be'
LM_HUB_FP = 'language_model/cv8be_5gram.bin'


def main(rate_audio_tuple: Tuple[int, np.ndarray]):
    sampling_rate, audio = rate_audio_tuple

    # resample audio to 16kHz
    resampler = Resample(orig_freq=sampling_rate, new_freq=16_000)
    audio_resampled = resampler(torch.tensor(audio)).numpy().flatten()

    # download Language Model from HF Hub
    lm_fp = hf_hub_download(repo_id=HF_HUB_URL, filename=LM_HUB_FP)

    # init pipeline
    pipeline = PreTrainedPipeline(model_path=HF_HUB_URL, language_model_fp=lm_fp)

    # recognize speech
    text_recognized = pipeline(inputs=audio_resampled)['text'][0]

    return text_recognized


iface = gr.Interface(
    fn=main,
    inputs='microphone',
    outputs="text"
)

iface.launch()