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library_name: transformers
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tags: []
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#
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## Model Details
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### Model Sources [optional]
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###
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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library_name: transformers
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tags: [DAC, audio]
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# Descript Audio Codec (.dac): High-Fidelity Audio Compression with Improved RVQGAN
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This repository contains training and inference scripts for the Descript Audio Codec (.dac), a high fidelity general neural audio codec, introduced in the paper titled **High-Fidelity Audio Compression with Improved RVQGAN**.
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[arXiv Paper: High-Fidelity Audio Compression with Improved RVQGAN
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](http://arxiv.org/abs/2306.06546) <br>
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[Demo Site](https://descript.notion.site/Descript-Audio-Codec-11389fce0ce2419891d6591a68f814d5)<br>
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👉 With Descript Audio Codec, you can compress **44.1 KHz audio** into discrete codes at a **low 8 kbps bitrate**. <br>
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🤌 That's approximately **90x compression** while maintaining exceptional fidelity and minimizing artifacts. <br>
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💪 Descript universal model works on all domains (speech, environment, music, etc.), making it widely applicable to generative modeling of all audio. <br>
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👌 It can be used as a drop-in replacement for EnCodec for all audio language modeling applications (such as AudioLMs, MusicLMs, MusicGen, etc.) <br>
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## Original Usage
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### Installation
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```
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pip install descript-audio-codec
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```
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OR
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pip install git+https://github.com/descriptinc/descript-audio-codec
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```
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### Weights
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Weights are released as part of this repo under MIT license.
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We release weights for models that can natively support 16 kHz, 24kHz, and 44.1kHz sampling rates.
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Weights are automatically downloaded when you first run `encode` or `decode` command. You can cache them using one of the following commands
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```bash
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python3 -m dac download # downloads the default 44kHz variant
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python3 -m dac download --model_type 44khz # downloads the 44kHz variant
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python3 -m dac download --model_type 24khz # downloads the 24kHz variant
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python3 -m dac download --model_type 16khz # downloads the 16kHz variant
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```
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We provide a Dockerfile that installs all required dependencies for encoding and decoding. The build process caches the default model weights inside the image. This allows the image to be used without an internet connection. [Please refer to instructions below.](#docker-image)
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### Compress audio
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```
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python3 -m dac encode /path/to/input --output /path/to/output/codes
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```
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This command will create `.dac` files with the same name as the input files.
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It will also preserve the directory structure relative to input root and
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re-create it in the output directory. Please use `python -m dac encode --help`
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for more options.
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### Reconstruct audio from compressed codes
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```
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python3 -m dac decode /path/to/output/codes --output /path/to/reconstructed_input
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```
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This command will create `.wav` files with the same name as the input files.
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It will also preserve the directory structure relative to input root and
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re-create it in the output directory. Please use `python -m dac decode --help`
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for more options.
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### Programmatic Usage
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```py
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import dac
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from audiotools import AudioSignal
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# Download a model
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model_path = dac.utils.download(model_type="44khz")
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model = dac.DAC.load(model_path)
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model.to('cuda')
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# Load audio signal file
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signal = AudioSignal('input.wav')
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# Encode audio signal as one long file
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# (may run out of GPU memory on long files)
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signal.to(model.device)
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x = model.preprocess(signal.audio_data, signal.sample_rate)
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z, codes, latents, _, _ = model.encode(x)
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# Decode audio signal
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y = model.decode(z)
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# Alternatively, use the `compress` and `decompress` functions
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# to compress long files.
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signal = signal.cpu()
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x = model.compress(signal)
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# Save and load to and from disk
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x.save("compressed.dac")
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x = dac.DACFile.load("compressed.dac")
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# Decompress it back to an AudioSignal
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y = model.decompress(x)
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# Write to file
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y.write('output.wav')
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```
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