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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", streaming=True),
outputs=["text","textbox"],
title="UTMOS Demo",
description=description,
allow_flagging=False,
).launch() |