parler-tts
/

dac_44khZ_8kbps

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 ---
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 ---
 library_name: transformers
+tags: [DAC, audio]
 ---
+# Descript Audio Codec (.dac): High-Fidelity Audio Compression with Improved RVQGAN
+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**.
+[arXiv Paper: High-Fidelity Audio Compression with Improved RVQGAN
+](http://arxiv.org/abs/2306.06546) <br>
+[Demo Site](https://descript.notion.site/Descript-Audio-Codec-11389fce0ce2419891d6591a68f814d5)<br>
+👉 With Descript Audio Codec, you can compress **44.1 KHz audio** into discrete codes at a **low 8 kbps bitrate**.  <br>
+🤌 That's approximately **90x compression** while maintaining exceptional fidelity and minimizing artifacts.  <br>
+💪 Descript universal model works on all domains (speech, environment, music, etc.), making it widely applicable to generative modeling of all audio.  <br>
+👌 It can be used as a drop-in replacement for EnCodec for all audio language modeling applications (such as AudioLMs, MusicLMs, MusicGen, etc.) <br>
+## Original Usage
+### Installation
+```
+pip install descript-audio-codec
+```
+OR
+```
+pip install git+https://github.com/descriptinc/descript-audio-codec
+```
+### Weights
+Weights are released as part of this repo under MIT license.
+We release weights for models that can natively support 16 kHz, 24kHz, and 44.1kHz sampling rates.
+Weights are automatically downloaded when you first run `encode` or `decode` command. You can cache them using one of the following commands
+```bash
+python3 -m dac download # downloads the default 44kHz variant
+python3 -m dac download --model_type 44khz # downloads the 44kHz variant
+python3 -m dac download --model_type 24khz # downloads the 24kHz variant
+python3 -m dac download --model_type 16khz # downloads the 16kHz variant
+```
+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)
+### Compress audio
+```
+python3 -m dac encode /path/to/input --output /path/to/output/codes
+```
+This command will create `.dac` files with the same name as the input files.
+It will also preserve the directory structure relative to input root and
+re-create it in the output directory. Please use `python -m dac encode --help`
+for more options.
+### Reconstruct audio from compressed codes
+```
+python3 -m dac decode /path/to/output/codes --output /path/to/reconstructed_input
+```
+This command will create `.wav` files with the same name as the input files.
+It will also preserve the directory structure relative to input root and
+re-create it in the output directory. Please use `python -m dac decode --help`
+for more options.
+### Programmatic Usage
+```py
+import dac
+from audiotools import AudioSignal
+# Download a model
+model_path = dac.utils.download(model_type="44khz")
+model = dac.DAC.load(model_path)
+model.to('cuda')
+# Load audio signal file
+signal = AudioSignal('input.wav')
+# Encode audio signal as one long file
+# (may run out of GPU memory on long files)
+signal.to(model.device)
+x = model.preprocess(signal.audio_data, signal.sample_rate)
+z, codes, latents, _, _ = model.encode(x)
+# Decode audio signal
+y = model.decode(z)
+# Alternatively, use the `compress` and `decompress` functions
+# to compress long files.
+signal = signal.cpu()
+x = model.compress(signal)
+# Save and load to and from disk
+x.save("compressed.dac")
+x = dac.DACFile.load("compressed.dac")
+# Decompress it back to an AudioSignal
+y = model.decompress(x)
+# Write to file
+y.write('output.wav')
+```