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Convert clips to 16-bit PCM before saving [skip ci]

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dscripka 2023-09-05 07:58:37 -04:00
parent 0620cd0835
commit 83d8bae37c
1 changed files with 15 additions and 10 deletions
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25
notebooks/automatic_model_training.ipynb
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@ -68,12 +68,12 @@
},
{
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"execution_count": 91,
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@ -127,8 +127,8 @@
"id": "58e4811e",
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"outputs": [],
@ -141,9 +141,10 @@
" os.mkdir(output_dir)\n",
"rir_dataset = datasets.load_dataset(\"davidscripka/MIT_environmental_impulse_responses\", split=\"train\", streaming=True)\n",
"\n",
"# Save clips to 16-bit PCM wav files\n",
"for row in tqdm(rir_dataset):\n",
" name = row['audio']['path'].split('/')[-1]\n",
" scipy.io.wavfile.write(os.path.join(output_dir, name), 16000, row['audio']['array'])\n"
" scipy.io.wavfile.write(os.path.join(output_dir, name), 16000, (row['audio']['array']*32767).astype(np.int16))\n"
]
},
{
@ -157,8 +158,10 @@
"\n",
"# Audioset Dataset (https://research.google.com/audioset/dataset/index.html)\n",
"# Download one part of the audioset .tar files, extract, and convert to 16khz\n",
"# For full-scale training, it's reccomended to download the entire dataset, and\n",
"# even combine with other background noise datasets (e.g., FSD50k, Freesound, etc.)\n",
"# For full-scale training, it's recommended to download the entire dataset from \n",
"# https://huggingface.co/datasets/agkphysics/AudioSet, and\n",
"# even potentially combine it with other background noise datasets (e.g., FSD50k, Freesound, etc.)\n",
"\n",
"fname = \"bal_train09.tar\"\n",
"out_dir = f\"audioset/{fname}\"\n",
"link = \"https://huggingface.co/datasets/agkphysics/AudioSet/resolve/main/\" + fname\n",
@ -172,11 +175,12 @@
"if not os.path.exists(output_dir):\n",
" os.mkdir(output_dir)\n",
"\n",
"# Save clips to 16-bit PCM wav files\n",
"audioset_dataset = datasets.Dataset.from_dict({\"audio\": [str(i) for i in Path(\"audioset/audio\").glob(\"**/*.flac\")]})\n",
"audioset_dataset = audioset_dataset.cast_column(\"audio\", datasets.Audio(sampling_rate=16000))\n",
"for row in tqdm(audioset_dataset):\n",
" name = row['audio']['path'].split('/')[-1].replace(\".flac\", \".wav\")\n",
" scipy.io.wavfile.write(os.path.join(output_dir, name), 16000, row['audio']['array'])\n",
" scipy.io.wavfile.write(os.path.join(output_dir, name), 16000, (row['audio']['array']*32767).astype(np.int16))\n",
"\n",
"# Free Music Archive dataset\n",
"# https://github.com/mdeff/fma\n",
@ -187,11 +191,12 @@
"fma_dataset = datasets.load_dataset(\"rudraml/fma\", name=\"small\", split=\"train\", streaming=True)\n",
"fma_dataset = iter(fma_dataset.cast_column(\"audio\", datasets.Audio(sampling_rate=16000)))\n",
"\n",
"# Save clips to 16-bit PCM wav files\n",
"n_hours = 1 # use only 1 hour of clips for this example notebook, recommend increasing for full-scale training\n",
"for i in tqdm(range(n_hours*3600//30)): # this works because the FMA dataset is all 30 second clips\n",
" row = next(fma_dataset)\n",
" name = row['audio']['path'].split('/')[-1].replace(\".mp3\", \".wav\")\n",
" scipy.io.wavfile.write(os.path.join(output_dir, name), 16000, row['audio']['array'])\n",
" scipy.io.wavfile.write(os.path.join(output_dir, name), 16000, (row['audio']['array']*32767).astype(np.int16))\n",
" i += 1\n",
" if i == n_hours*3600//30:\n",
" break\n"