| Name | xares JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | eXtensive Audio Representation and Evaluation Suite |
| upload_time | 2024-09-01 06:04:24 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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# xares
X-ARES: eXtensive Audio Representation and Evaluation Suite
**This project is still in the early stage of development and welcomes contributions, especially in expanding supported tasks and improving robustness.**
## Introduction
X-ARES is a toolkit for training, evaluating, and exporting audio encoders for various audio tasks. It is heavily inspired by the [HEAR benchmark](https://hearbenchmark.com/).
## Planned supported tasks
Currently, only four tasks (ESC-50, VocalSound, CREMA-D and LibriSpeech-Male-Female) have been implemented. However, adding new tasks is straightforward: it typically only need to implement the audio data download and packaging functions, as manual audio decoding and training implementations are not necessary. We encourage anyone to submit pull requests to expand the set of supported tasks.
### Speech
- Speech Commands V1
- Speech Commands V2
- LibriCount
- VoxLingua107
- VoxCeleb1
- LibriSpeech-Male-Female
- LibriSpeech-Phoneme
- Fluent Speech Commands
- VocalSound
- CREMA-D
- RAVDESS
- ASV2015
- DiCOVA
- speechocean762
### Environment
- ESC-50
- FSD50k
- UrbanSound 8k
- DESED
- (A task designed for car)
- (Another task designed for car)
- (A task designed for soundbox)
- (A task designed for headphone)
- LITIS Rouen
- FSD18-Kaggle
- AudioCaps
### Music
- MAESTRO
- GTZAN Genre
- NSynth
- MTG-Jamendo
- FMA
## Installation
X-ARES is available on [PyPI](https://pypi.org/project/xares/). You can install it via pip.
```bash
pip install xares
```
For development, you can clone the repository and install the package in editable mode.
```bash
git clone <this-repo>
cd xares
pip install -e .[examples]
```
## Run with the baseline pretrained audio encoder (Dasheng)
The ESC-50 task is used as an example.
```python
from example.dasheng.dasheng_encoder import DashengEncoder
from tasks.esc50 import esc50_task
task = esc50_task.ESC50Task(env_root="./env", encoder=DashengEncoder())
task.run_all()
```
## Run with your own pretrained audio encoder
An example of audio encoder wrapper could be found at `example/dasheng/dasheng_encoder.py`. It is very simple because the "dasheng" model is already in the required in/out format.
```python
from dataclasses import dataclass
from dasheng import dasheng_base
from xares.audio_encoder_base import AudioEncoderBase
@dataclass
class DashengEncoder(AudioEncoderBase):
model = dasheng_base()
sampling_rate = 16_000
output_dim = 768
def __call__(self, audio, sampling_rate):
# Since the "dasheng" model is already in the required in/out format, we directly use the super class method
return super().__call__(audio, sampling_rate)
```
Another example could be found at `example/wav2vec2/wav2vec2.py`. It is more complex, you need to covert the input audio and the encoded embedding to the required format.
```python
from dataclasses import dataclass
from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2Model
from xares.audio_encoder_base import AudioEncoderBase
@dataclass
class Wav2vec2Encoder(AudioEncoderBase):
output_dim = 768
def __post_init__(self):
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
self.model = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h")
def __call__(self, audio, sampling_rate):
input_values = (
self.feature_extractor(
self.pre_process_audio(audio, sampling_rate), sampling_rate=self.sampling_rate, return_tensors="pt"
)
.input_values.squeeze()
.to(self.device)
)
encoded_audio = self.encode_audio(input_values)["last_hidden_state"]
if not self.check_encoded_audio(encoded_audio):
raise ValueError("Invalid encoded audio")
return self.encoded_audio
```
## Add your own task
To add a new task, refer to existing task implementations for guidance. Essentially, create a task class that inherits from `TaskBase` and implements the `make_audio_tar` method tailored to your chosen dataset, handling data download, preprocessing, and packaging into the required format.
Raw data
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"author_email": "Junbo Zhang <zhangjunbo1@xiaomi.com>, Chenyu Liu <liuchenyu3@xiaomi.com>, Heinrich Dinkel <dinkelheinrich@xiaomi.com>",
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"description": "# xares\n\nX-ARES: eXtensive Audio Representation and Evaluation Suite\n\n**This project is still in the early stage of development and welcomes contributions, especially in expanding supported tasks and improving robustness.**\n\n## Introduction\n\nX-ARES is a toolkit for training, evaluating, and exporting audio encoders for various audio tasks. It is heavily inspired by the [HEAR benchmark](https://hearbenchmark.com/).\n\n## Planned supported tasks\n\nCurrently, only four tasks (ESC-50, VocalSound, CREMA-D and LibriSpeech-Male-Female) have been implemented. However, adding new tasks is straightforward: it typically only need to implement the audio data download and packaging functions, as manual audio decoding and training implementations are not necessary. We encourage anyone to submit pull requests to expand the set of supported tasks.\n\n### Speech\n\n- Speech Commands V1\n- Speech Commands V2\n- LibriCount\n- VoxLingua107\n- VoxCeleb1\n- LibriSpeech-Male-Female\n- LibriSpeech-Phoneme\n- Fluent Speech Commands\n- VocalSound\n- CREMA-D\n- RAVDESS\n- ASV2015\n- DiCOVA\n- speechocean762\n\n### Environment\n\n- ESC-50\n- FSD50k\n- UrbanSound 8k\n- DESED\n- (A task designed for car)\n- (Another task designed for car)\n- (A task designed for soundbox)\n- (A task designed for headphone)\n- LITIS Rouen\n- FSD18-Kaggle\n- AudioCaps\n\n### Music\n\n- MAESTRO\n- GTZAN Genre\n- NSynth\n- MTG-Jamendo\n- FMA\n\n## Installation\n\nX-ARES is available on [PyPI](https://pypi.org/project/xares/). You can install it via pip.\n\n```bash\npip install xares\n```\n\nFor development, you can clone the repository and install the package in editable mode.\n\n```bash\ngit clone <this-repo>\ncd xares\npip install -e .[examples]\n```\n\n## Run with the baseline pretrained audio encoder (Dasheng)\n\nThe ESC-50 task is used as an example.\n\n```python\nfrom example.dasheng.dasheng_encoder import DashengEncoder\nfrom tasks.esc50 import esc50_task\n\ntask = esc50_task.ESC50Task(env_root=\"./env\", encoder=DashengEncoder())\n\ntask.run_all()\n```\n\n## Run with your own pretrained audio encoder\n\nAn example of audio encoder wrapper could be found at `example/dasheng/dasheng_encoder.py`. It is very simple because the \"dasheng\" model is already in the required in/out format.\n\n```python\nfrom dataclasses import dataclass\nfrom dasheng import dasheng_base\nfrom xares.audio_encoder_base import AudioEncoderBase\n\n\n@dataclass\nclass DashengEncoder(AudioEncoderBase):\n model = dasheng_base()\n sampling_rate = 16_000\n output_dim = 768\n\n def __call__(self, audio, sampling_rate):\n # Since the \"dasheng\" model is already in the required in/out format, we directly use the super class method\n return super().__call__(audio, sampling_rate)\n```\n\nAnother example could be found at `example/wav2vec2/wav2vec2.py`. It is more complex, you need to covert the input audio and the encoded embedding to the required format.\n\n```python\nfrom dataclasses import dataclass\nfrom transformers import Wav2Vec2FeatureExtractor, Wav2Vec2Model\nfrom xares.audio_encoder_base import AudioEncoderBase\n\n\n@dataclass\nclass Wav2vec2Encoder(AudioEncoderBase):\n output_dim = 768\n\n def __post_init__(self):\n self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(\"facebook/wav2vec2-base-960h\")\n self.model = Wav2Vec2Model.from_pretrained(\"facebook/wav2vec2-base-960h\")\n\n def __call__(self, audio, sampling_rate):\n input_values = (\n self.feature_extractor(\n self.pre_process_audio(audio, sampling_rate), sampling_rate=self.sampling_rate, return_tensors=\"pt\"\n )\n .input_values.squeeze()\n .to(self.device)\n )\n\n encoded_audio = self.encode_audio(input_values)[\"last_hidden_state\"]\n\n if not self.check_encoded_audio(encoded_audio):\n raise ValueError(\"Invalid encoded audio\")\n\n return self.encoded_audio\n```\n\n## Add your own task\n\nTo add a new task, refer to existing task implementations for guidance. Essentially, create a task class that inherits from `TaskBase` and implements the `make_audio_tar` method tailored to your chosen dataset, handling data download, preprocessing, and packaging into the required format.\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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