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https://github.com/zabir-nabil/torch-speech-dataloader
A ready-to-use pytorch dataloader for audio classification, speech classification, speaker recognition, etc. with in-GPU augmentations
https://github.com/zabir-nabil/torch-speech-dataloader
audio-augmentation gpu-augmentation pytorch-speech-dataloader speech speech-augmentation-gpu torch torch-dataloader
Last synced: about 1 month ago
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A ready-to-use pytorch dataloader for audio classification, speech classification, speaker recognition, etc. with in-GPU augmentations
- Host: GitHub
- URL: https://github.com/zabir-nabil/torch-speech-dataloader
- Owner: zabir-nabil
- License: mit
- Created: 2022-09-12T09:26:27.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-11-06T09:18:10.000Z (about 2 years ago)
- Last Synced: 2023-02-27T21:13:57.393Z (almost 2 years ago)
- Topics: audio-augmentation, gpu-augmentation, pytorch-speech-dataloader, speech, speech-augmentation-gpu, torch, torch-dataloader
- Language: Python
- Homepage:
- Size: 429 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## torch-speech-dataloader
A ready-to-use pytorch dataloader for audio classification, speech classification, speaker recognition, etc. with in-GPU augmentations.
* PyTorch speech dataloader with 5 (or less) lines of code. `get_torch_speech_dataloader_from_config(config)`
* Batch augmentation in GPU, powered by [torch-audiomentations](https://github.com/asteroid-team/torch-audiomentations)
* RIRs augmentation with any set of IR file(s) [*cpu*]
* MUSAN-like augmentation with any set of source files. Customizable. [*cpu*]
* Written in one night, may contain bugs!
## Install
```cmd
pip install -U git+https://github.com/zabir-nabil/torch-speech-dataloader.git@main
```
## Use
```python
from torch_speech_dataloader import get_torch_speech_dataloader, get_torch_speech_dataloader_from_config
from torch_speech_dataloader.augmentation_utils import placeholder_gpu_augmentation
config_1 = {
"filenames" : ["../test.wav"] * 5 + ["../test_hindi.wav"] * 5,
"speech_labels" : ["test"] * 5 + ["test2"] * 5,
"batch_size" : 3,
"num_workers" : 5,
"device" : torch.device('cuda:1'),
"sanity_check_path" : "../sanity_test",
"sanity_check_samples" : 2,
"batch_audio_augmentation": placeholder_gpu_augmentation,
"rirs_reverb" : {"apply": True},
"musan_augmentation" : {"apply": True, "mix_multiples_max_count": -1, "musan_max_len": 1.},
"verbose" : 0
}
dummy_tsdl = get_torch_speech_dataloader_from_config(config_1)
for d, l in dummy_tsdl.get_batch():
print(d.shape)
print(l)
```
## Others
#### `config` parameters
* `filenames`: A list of filepaths for the audio / speech files (usually wav).
* `speech_labels`: Corresponding labels for `filenames` / list of audio files.
* `batch_size`: Batch size of the dataloader.
* `num_workers`: Dataloader workers.
* `device`: torch device [default: *cpu*].
* `sanity_check_path`: If you want to look at the sample audio files generated, specify a path where the sample augmented audio files will be saved.
* `sanity_check_samples`: Number of sample audio files to store in the sanity check folder.
* `batch_audio_augmentation`: Usually, it will run on the GPU batch if gpu device is specified, else on the CPU batch. Any transform (compose) / augmentation, that takes a tensor of dimension **[B x C x N]**.
* `rirs_reverb`:
* `apply`: If apply is true, only then this augmentation will be applied to each audio individually.
* `reverb_source_files_path`: A list of IR filepaths.
* `musan_augmentation`:
* `apply`: If apply is true, only then this augmentation will be applied to each audio individually.
* `musan_config`:
```{
"music": ([list of music file paths], range_for_num_music_files_to_use, range_for_noise_snr),
"speech": ([list of speech file paths], range_for_num_speech_files_to_use, range_for_noise_snr),
}``` `[example: augmentation_utils.placeholder_musan_config]`
* `mix_multiples_max_count`: Multiple noise types should be mixed (music + noise + `...`). Number of noise types that should be mixed at most.
* `musan_max_len`: `<= 0`: take the musan noise and crop it with equal length (same as input audio); `> 0`: maximum length of the cropped musan noise (in secs.).
* `audio_augmentation`: List of `func`s that can be applied to a single audio with shape **[N,]**.
* `features`: Feature extraction. **[N,]** -> **[T,F]**.
* `feature_augmentation`: List of `func`s that can be applied to a single feature with shape **[T,F]**.