Chunked inference for codec

Dockerfile.dev

@@ -0,0 +1,13 @@
+ARG IMAGE=pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
+ARG GITHUB_TOKEN=none
+
+FROM $IMAGE
+
+RUN echo machine github.com login ${GITHUB_TOKEN} > ~/.netrc
+
+COPY requirements.txt /requirements.txt
+
+RUN apt update && apt install -y git
+
+# install the package
+RUN pip install --upgrade -r /requirements.txt

README.md

@@ -66,33 +66,42 @@ for more options.
 ### Programmatic Usage
 ```py
 import dac
-from dac.utils import load_model
-from dac.model import DAC
-
-from dac.utils.encode import process as encode
-from dac.utils.decode import process as decode
-
 from audiotools import AudioSignal
 
-# Init an empty model
-model = DAC()
+# Download a model
+model_path = dac.utils.download(model_type="44khz")
+model = dac.DAC.load(model_path)
 
-# Load compatible pre-trained model
-model = load_model(tag="latest", model_type="44khz")
-model.eval()
 model.to('cuda')
 
 # Load audio signal file
 signal = AudioSignal('input.wav')
 
-# Encode audio signal
-encoded_out = encode(signal, 'cuda', model)
+# 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
-recon = decode(encoded_out, 'cuda', model, preserve_sample_rate=True)
+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
-recon.write('recon.wav')
+y.write('output.wav')
 ```
 
 ### Docker image
@@ -131,6 +140,28 @@ Please install the correct dependencies
 pip install -e ".[dev]"
 ```
 
+## Environment setup
+
+We have provided a Dockerfile and docker compose setup that makes running experiments easy.
+
+To build the docker image do:
+
+```
+docker compose build
+```
+
+Then, to launch a container, do:
+
+```
+docker compose run -p 8888:8888 -p 6006:6006 dev
+```
+
+The port arguments (`-p`) are optional, but useful if you want to launch a Jupyter and Tensorboard instances within the container. The
+default password for Jupyter is `password`, and the current directory
+is mounted to `/u/home/src`, which also becomes the working directory.
+
+Then, run your training command.
+
 
 ### Single GPU training
 ```

conf/final/44khz-16kbps.yml

@@ -0,0 +1,124 @@
+# Model setup
+DAC.sample_rate: 44100
+DAC.encoder_dim: 64
+DAC.encoder_rates: [2, 4, 8, 8]
+DAC.latent_dim: 128
+DAC.decoder_dim: 1536
+DAC.decoder_rates: [8, 8, 4, 2]
+
+# Quantization
+DAC.n_codebooks: 18 # Max bitrate of 16kbps
+DAC.codebook_size: 1024
+DAC.codebook_dim: 8
+DAC.quantizer_dropout: 0.5
+
+# Discriminator
+Discriminator.sample_rate: 44100
+Discriminator.rates: []
+Discriminator.periods: [2, 3, 5, 7, 11]
+Discriminator.fft_sizes: [2048, 1024, 512]
+Discriminator.bands:
+  - [0.0, 0.1]
+  - [0.1, 0.25]
+  - [0.25, 0.5]
+  - [0.5, 0.75]
+  - [0.75, 1.0]
+
+# Optimization
+AdamW.betas: [0.8, 0.99]
+AdamW.lr: 0.0001
+ExponentialLR.gamma: 0.999996
+
+amp: false
+val_batch_size: 100
+device: cuda
+num_iters: 400000
+save_iters: [10000, 50000, 100000, 200000]
+valid_freq: 1000
+sample_freq: 10000
+num_workers: 32
+val_idx: [0, 1, 2, 3, 4, 5, 6, 7]
+seed: 0
+lambdas:
+  mel/loss: 15.0
+  adv/feat_loss: 2.0
+  adv/gen_loss: 1.0
+  vq/commitment_loss: 0.25
+  vq/codebook_loss: 1.0
+
+VolumeNorm.db: [const, -16]
+
+# Transforms
+build_transform.preprocess:
+  - Identity
+build_transform.augment_prob: 0.0
+build_transform.augment:
+  - Identity
+build_transform.postprocess:
+  - VolumeNorm
+  - RescaleAudio
+  - ShiftPhase
+
+# Loss setup
+MultiScaleSTFTLoss.window_lengths: [2048, 512]
+MelSpectrogramLoss.n_mels: [5, 10, 20, 40, 80, 160, 320]
+MelSpectrogramLoss.window_lengths: [32, 64, 128, 256, 512, 1024, 2048]
+MelSpectrogramLoss.mel_fmin: [0, 0, 0, 0, 0, 0, 0]
+MelSpectrogramLoss.mel_fmax: [null, null, null, null, null, null, null]
+MelSpectrogramLoss.pow: 1.0
+MelSpectrogramLoss.clamp_eps: 1.0e-5
+MelSpectrogramLoss.mag_weight: 0.0
+
+# Data
+batch_size: 72
+train/AudioDataset.duration: 0.38
+train/AudioDataset.n_examples: 10000000
+
+val/AudioDataset.duration: 5.0
+val/build_transform.augment_prob: 1.0
+val/AudioDataset.n_examples: 250
+
+test/AudioDataset.duration: 10.0
+test/build_transform.augment_prob: 1.0
+test/AudioDataset.n_examples: 1000
+
+AudioLoader.shuffle: true
+AudioDataset.without_replacement: true
+
+train/build_dataset.folders:
+  speech_fb:
+    - /data/daps/train
+  speech_hq:
+    - /data/vctk
+    - /data/vocalset
+    - /data/read_speech
+    - /data/french_speech
+  speech_uq:
+    - /data/emotional_speech/
+    - /data/common_voice/
+    - /data/german_speech/
+    - /data/russian_speech/
+    - /data/spanish_speech/
+  music_hq:
+    - /data/musdb/train
+  music_uq:
+    - /data/jamendo
+  general:
+    - /data/audioset/data/unbalanced_train_segments/
+    - /data/audioset/data/balanced_train_segments/
+
+val/build_dataset.folders:
+  speech_hq:
+    - /data/daps/val
+  music_hq:
+    - /data/musdb/test
+  general:
+    - /data/audioset/data/eval_segments/
+
+test/build_dataset.folders:
+  speech_hq:
+    - /data/daps/test
+  music_hq:
+    - /data/musdb/test
+  general:
+    - /data/audioset/data/eval_segments/

dac/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.0.5"
+__version__ = "1.0.0"
 
 # preserved here for legacy reasons
 __model_version__ = "latest"
@@ -11,3 +11,6 @@
 
 from . import nn
 from . import model
+from . import utils
+from .model import DAC
+from .model import DACFile

dac/__main__.py

@@ -2,7 +2,7 @@
 
 import argbind
 
-from dac.utils import ensure_default_model as download
+from dac.utils import download
 from dac.utils.decode import decode
 from dac.utils.encode import encode
 

dac/model/__init__.py

@@ -1,3 +1,4 @@
 from .base import CodecMixin
+from .base import DACFile
 from .dac import DAC
 from .discriminator import Discriminator