app_record.py

from random import sample
import gradio as gr
import torchaudio
import torch
import torch.nn as nn
import lightning_module
import pdb

# ASR part
from transformers import pipeline
p = pipeline("automatic-speech-recognition")

class ChangeSampleRate(nn.Module):
    def __init__(self, input_rate: int, output_rate: int):
        super().__init__()
        self.output_rate = output_rate
        self.input_rate = input_rate

    def forward(self, wav: torch.tensor) -> torch.tensor:
        # Only accepts 1-channel waveform input
        wav = wav.view(wav.size(0), -1)
        new_length = wav.size(-1) * self.output_rate // self.input_rate
        indices = (torch.arange(new_length) * (self.input_rate / self.output_rate))
        round_down = wav[:, indices.long()]
        round_up = wav[:, (indices.long() + 1).clamp(max=wav.size(-1) - 1)]
        output = round_down * (1. - indices.fmod(1.)).unsqueeze(0) + round_up * indices.fmod(1.).unsqueeze(0)
        return output

model = lightning_module.BaselineLightningModule.load_from_checkpoint("epoch=3-step=7459.ckpt").eval()
def calc_mos(audio_path):
    wav, sr = torchaudio.load(audio_path)
    osr = 16_000
    batch = wav.unsqueeze(0).repeat(10, 1, 1)
    csr = ChangeSampleRate(sr, osr)
    out_wavs = csr(wav)
    
    transcription = p(audio_path)["text"]
    batch = {
        'wav': out_wavs,
        'domains': torch.tensor([0]),
        'judge_id': torch.tensor([288])
    }
    with torch.no_grad():
        output = model(batch)
    return output.mean(dim=1).squeeze().detach().numpy()*2 + 3, transcription


description ="""
MOS prediction demo using UTMOS-strong w/o phoneme encoder model, which is trained on the main track dataset.
This demo only accepts .wav format. Best at 16 kHz sampling rate.

Paper is available [here](https://arxiv.org/abs/2204.02152)
""" 

# inputs=gr.inputs.Audio(type='filepath'),
iface = gr.Interface(
  fn=calc_mos,
  inputs = gr.Audio(source="microphone", type="filepath"), 
  outputs=["text","textbox"],
  title="UTMOS Demo",
  description=description,
  allow_flagging=True,

).launch()