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import pathlib |
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import yaml |
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import torch |
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import torchaudio |
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import numpy as np |
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from lightning_module import SSLDualLightningModule |
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import gradio as gr |
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import subprocess |
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import requests |
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def normalize_waveform(wav, sr, db=-3): |
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wav, _ = torchaudio.sox_effects.apply_effects_tensor( |
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wav.unsqueeze(0), |
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sr, |
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[["norm", "{}".format(db)]], |
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) |
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return wav.squeeze(0) |
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def download_file_from_google_drive(id, destination): |
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URL = "https://docs.google.com/uc?export=download" |
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session = requests.Session() |
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response = session.get(URL, params = { 'id' : id }, stream = True) |
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token = get_confirm_token(response) |
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if token: |
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params = { 'id' : id, 'confirm' : token } |
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response = session.get(URL, params = params, stream = True) |
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save_response_content(response, destination) |
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def get_confirm_token(response): |
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for key, value in response.cookies.items(): |
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if key.startswith('download_warning'): |
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return value |
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return None |
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def save_response_content(response, destination): |
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CHUNK_SIZE = 32768 |
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with open(destination, "wb") as f: |
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for chunk in response.iter_content(CHUNK_SIZE): |
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if chunk: |
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f.write(chunk) |
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def calc_spectrogram(wav, config): |
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spec_module = torchaudio.transforms.MelSpectrogram( |
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sample_rate=config["preprocess"]["sampling_rate"], |
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n_fft=config["preprocess"]["fft_length"], |
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win_length=config["preprocess"]["frame_length"], |
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hop_length=config["preprocess"]["frame_shift"], |
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f_min=config["preprocess"]["fmin"], |
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f_max=config["preprocess"]["fmax"], |
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n_mels=config["preprocess"]["n_mels"], |
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power=1, |
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center=True, |
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norm="slaney", |
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mel_scale="slaney", |
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) |
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specs = spec_module(wav) |
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log_spec = torch.log( |
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torch.clamp_min(specs, config["preprocess"]["min_magnitude"]) |
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* config["preprocess"]["comp_factor"] |
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).to(torch.float32) |
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return log_spec |
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def transfer(audio): |
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wp_src = pathlib.Path("aet_sample/src.wav") |
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wav_src, sr = torchaudio.load(wp_src) |
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sr_inp, wav_tar = audio |
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wav_tar = wav_tar / (np.max(np.abs(wav_tar)) * 1.1) |
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wav_tar = torch.from_numpy(wav_tar.astype(np.float32)) |
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resampler = torchaudio.transforms.Resample( |
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orig_freq=sr_inp, |
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new_freq=sr, |
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) |
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wav_tar = resampler(wav_tar) |
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config_path = pathlib.Path("configs/test/melspec/ssl_tono.yaml") |
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config = yaml.load(open(config_path, "r"), Loader=yaml.FullLoader) |
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melspec_src = calc_spectrogram( |
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normalize_waveform(wav_src.squeeze(0), sr), config |
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) |
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wav_tar = normalize_waveform(wav_tar.squeeze(0), sr) |
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ckpt_path = pathlib.Path("tono_aet_melspec.ckpt").resolve() |
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src_model = SSLDualLightningModule(config).load_from_checkpoint( |
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checkpoint_path=ckpt_path, |
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config=config, |
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) |
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encoder_src = src_model.encoder |
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channelfeats_src = src_model.channelfeats |
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channel_src = src_model.channel |
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_, enc_hidden_src = encoder_src( |
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melspec_src.unsqueeze(0).unsqueeze(1).transpose(2, 3) |
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) |
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chfeats_src = channelfeats_src(enc_hidden_src) |
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wav_transfer = channel_src(wav_tar.unsqueeze(0), chfeats_src) |
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wav_transfer = wav_transfer.detach().numpy()[0, :] |
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return sr, wav_transfer |
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if __name__ == "__main__": |
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subprocess.run(["curl", "-OL", "https://sarulab.sakura.ne.jp/saeki/selfremaster/pretrained/tono_aet_melspec.ckpt"]) |
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download_file_from_google_drive("10OJ2iznutxzp8MEIS6lBVaIS_g5c_70V", "hifigan/hifigan_melspec_universal") |
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subprocess.run(["ls", "-a"]) |
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subprocess.run(["ls", "-a", "aet_sample"]) |
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iface = gr.Interface( |
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transfer, |
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"audio", |
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gr.outputs.Audio(type="numpy"), |
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examples=[ |
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["aet_sample/tar.wav"] |
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], |
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title='Audio effect transfer demo', |
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description='Add channel feature of Japanese old audio recording to any high-quality audio' |
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) |
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iface.launch() |
