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import gradio as gr
from miipher.dataset.preprocess_for_infer import PreprocessForInfer
from miipher.lightning_module import MiipherLightningModule
from lightning_vocoders.models.hifigan.xvector_lightning_module import HiFiGANXvectorLightningModule
import torch
import torchaudio
import hydra
import tempfile
miipher_path = "miipher.ckpt"
miipher = MiipherLightningModule.load_from_checkpoint(miipher_path,map_location='cpu')
vocoder = HiFiGANXvectorLightningModule.load_from_checkpoint("vocoder_finetuned.ckpt",map_location='cpu')
xvector_model = hydra.utils.instantiate(vocoder.cfg.data.xvector.model)
xvector_model = xvector_model.to('cpu')
preprocessor = PreprocessForInfer(miipher.cfg)
@torch.inference_mode()
def main(wav_path,transcript,lang_code):
wav,sr =torchaudio.load(wav_path)
wav = wav[0].unsqueeze(0)
batch = preprocessor.process(
'test',
(torch.tensor(wav),sr),
word_segmented_text=transcript,
lang_code=lang_code
)
miipher.feature_extractor(batch)
(
phone_feature,
speaker_feature,
degraded_ssl_feature,
_,
) = miipher.feature_extractor(batch)
cleaned_ssl_feature, _ = miipher(phone_feature,speaker_feature,degraded_ssl_feature)
vocoder_xvector = xvector_model.encode_batch(batch['degraded_wav_16k'].view(1,-1).cpu()).squeeze(1)
cleaned_wav = vocoder.generator_forward({"input_feature": cleaned_ssl_feature, "xvector": vocoder_xvector})[0].T
with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as fp:
torchaudio.save(fp,cleaned_wav.view(1,-1), sample_rate=22050,format='wav')
return fp.name
inputs = [gr.Audio(label="noisy audio",type='filepath'),gr.Textbox(label="Transcript", value="Your transcript here", max_lines=1),
gr.Radio(label="Language", choices=["eng-us", "jpn"], value="eng-us")]
outputs = gr.Audio(label="Output")
demo = gr.Interface(fn=main, inputs=inputs, outputs=outputs)
demo.launch()
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