{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/data2/waris/envs/darkstream/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"/mnt/data2/waris/envs/darkstream/lib/python3.10/site-packages/speechbrain/utils/torch_audio_backend.py:57: UserWarning: torchaudio._backend.list_audio_backends has been deprecated. This deprecation is part of a large refactoring effort to transition TorchAudio into a maintenance phase. The decoding and encoding capabilities of PyTorch for both audio and video are being consolidated into TorchCodec. Please see https://github.com/pytorch/audio/issues/3902 for more information. It will be removed from the 2.9 release. \n",
" available_backends = torchaudio.list_audio_backends()\n"
]
}
],
"source": [
"import os\n",
"import sys\n",
"sys.path.append('..')\n",
"import torch\n",
"import librosa\n",
"from scipy.io import wavfile\n",
"from IPython import display as disp\n",
"from pathlib import Path\n",
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from src.modules import VocosVocoderModule"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\" # Set the GPU device\n",
"MAX_WAV_VALUE = 32768.0"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/mnt/data2/waris/envs/darkstream/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n",
" WeightNorm.apply(module, name, dim)\n"
]
},
{
"data": {
"text/plain": [
"VocosVocoderModule(\n",
" (feature_extractor): StreamingLogMelSpectrogram(\n",
" (conv): RawStreamingLogMelSpectrogram(\n",
" (spectrogram): LinearSpectrogram()\n",
" )\n",
" )\n",
" (decoder): Sequential(\n",
" (0): VocosBackbone(\n",
" (embed): StreamingConv1d(\n",
" (conv): NormConv1d(\n",
" (conv): RawStreamingConv1d(80, 512, kernel_size=(7,), stride=(1,))\n",
" )\n",
" )\n",
" (norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
" (convnext): ModuleList(\n",
" (0-7): 8 x ConvNeXtBlock(\n",
" (dwconv): StreamingConv1d(\n",
" (conv): NormConv1d(\n",
" (conv): RawStreamingConv1d(512, 512, kernel_size=(7,), stride=(1,), groups=512)\n",
" )\n",
" )\n",
" (norm): LayerNorm()\n",
" (pwconv1): Linear(in_features=512, out_features=1536, bias=True)\n",
" (act): GELU(approximate='none')\n",
" (pwconv2): Linear(in_features=1536, out_features=512, bias=True)\n",
" (drop_path): Identity()\n",
" (add): StreamingAdd()\n",
" )\n",
" )\n",
" (final_layer_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)\n",
" )\n",
" (1): ISTFTHead(\n",
" (out): Linear(in_features=512, out_features=1026, bias=True)\n",
" (istft): StreamingISTFT(\n",
" (overlap_add): OverlapAdd1d(\n",
" (deconv): ConvTranspose1d(1024, 1, kernel_size=(1024,), stride=(320,), bias=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (discriminator): Discriminator(\n",
" (discriminators): ModuleList(\n",
" (0-4): 5 x MPD(\n",
" (convs): ModuleList(\n",
" (0): Sequential(\n",
" (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (1): Sequential(\n",
" (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (2): Sequential(\n",
" (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (3): Sequential(\n",
" (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (4): Sequential(\n",
" (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" )\n",
" (conv_post): Conv2d(1024, 1, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
" )\n",
" (5-7): 3 x MRD(\n",
" (band_convs): ModuleList(\n",
" (0-4): 5 x ModuleList(\n",
" (0): Sequential(\n",
" (0): Conv2d(2, 32, kernel_size=(3, 9), stride=(1, 1), padding=(1, 4))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (1-3): 3 x Sequential(\n",
" (0): Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (4): Sequential(\n",
" (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" )\n",
" )\n",
" (conv_post): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" (waveform_loss): L1Loss()\n",
" (stft_loss): MultiScaleSTFTLoss(\n",
" (loss_fn): L1Loss()\n",
" )\n",
" (mel_loss): MelSpectrogramLoss(\n",
" (loss_fn): L1Loss()\n",
" )\n",
" (gan_loss): GANLoss(\n",
" (discriminator): Discriminator(\n",
" (discriminators): ModuleList(\n",
" (0-4): 5 x MPD(\n",
" (convs): ModuleList(\n",
" (0): Sequential(\n",
" (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (1): Sequential(\n",
" (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (2): Sequential(\n",
" (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (3): Sequential(\n",
" (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (4): Sequential(\n",
" (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" )\n",
" (conv_post): Conv2d(1024, 1, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0))\n",
" )\n",
" (5-7): 3 x MRD(\n",
" (band_convs): ModuleList(\n",
" (0-4): 5 x ModuleList(\n",
" (0): Sequential(\n",
" (0): Conv2d(2, 32, kernel_size=(3, 9), stride=(1, 1), padding=(1, 4))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (1-3): 3 x Sequential(\n",
" (0): Conv2d(32, 32, kernel_size=(3, 9), stride=(1, 2), padding=(1, 4))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" (4): Sequential(\n",
" (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (1): LeakyReLU(negative_slope=0.1)\n",
" )\n",
" )\n",
" )\n",
" (conv_post): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (train_stft_loss): MeanMetric()\n",
" (train_mel_loss): MeanMetric()\n",
" (train_waveform_loss): MeanMetric()\n",
" (train_adv_gen_loss): MeanMetric()\n",
" (train_adv_feat_loss): MeanMetric()\n",
" (train_adv_d_loss): MeanMetric()\n",
" (val_stft_loss): MeanMetric()\n",
" (val_mel_loss): MeanMetric()\n",
" (val_waveform_loss): MeanMetric()\n",
" (test_loss): MeanMetric()\n",
" (val_loss_best): MinMetric()\n",
")"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = VocosVocoderModule.load_from_checkpoint(\n",
" '../ckpts/epoch=3.ckpt',\n",
" map_location=\"cpu\",\n",
")\n",
"\n",
"model.eval()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wav_file = \"../sample_wav/BDL/arctic_a0001.wav\"\n",
"_device = \"cpu\"\n",
"sample_rate = 16000\n",
"\n",
"audio, _ = librosa.load(wav_file, sr=sample_rate)\n",
"\n",
"disp.Audio(audio, rate=16000)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"audio = torch.from_numpy(audio).unsqueeze(0).unsqueeze(0).to(_device)\n",
"mel_spec = model.feature_extractor(audio)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# mel: torch.Tensor of shape (80, T)\n",
"mel_np = mel_spec.squeeze().cpu().numpy() # -> (80, T)\n",
"\n",
"plt.figure(figsize=(10, 4))\n",
"plt.imshow(mel_np, aspect='auto', origin='lower')\n",
"plt.xlabel('Time frames')\n",
"plt.ylabel('Mel filter bank channel')\n",
"plt.title('Mel‑spectrogram')\n",
"plt.colorbar(label='Amplitude')\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# mel to wav\n",
"with torch.no_grad():\n",
" audio_out = model(mel_spec)\n",
" audio_out = audio_out.squeeze().cpu().numpy()\n",
" # audio_out = audio_out * MAX_WAV_VALUE\n",
" # audio_out = audio_out.astype('int16')\n",
"\n",
"disp.Audio(audio_out, rate=16000)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 162/162 [00:01<00:00, 91.25it/s]\n"
]
},
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# streaming generation\n",
"chunk_size = 1\n",
"with torch.no_grad(), model.decoder[0].streaming(1), model.decoder[1].streaming(1):\n",
" audio_out_stream = []\n",
" for mel in tqdm(mel_spec.split(chunk_size, dim=2)):\n",
" audio_out_stream.append(model(mel))\n",
" audio_out_stream = torch.cat(audio_out_stream, dim=2).squeeze().cpu().numpy()\n",
"\n",
"disp.Audio(audio_out_stream, rate=16000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "darkstream",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.18"
}
},
"nbformat": 4,
"nbformat_minor": 2
}