Word2Wave: a framework for generating short audio samples from a text prompt using WaveGAN and COALA.

Overview

Word2Wave

Word2Wave is a simple method for text-controlled GAN audio generation. You can either follow the setup instructions below and use the source code and CLI provided in this repo or you can have a play around in the Colab notebook provided. Note that, in both cases, you will need to train a WaveGAN model first. You can also hear some examples here.

Colab playground Open In Colab

Setup

First, clone the repository

git clone https://www.github.com/ilaria-manco/word2wave

Create a virtual environment and install the requirements:

cd word2wave
python3 -m venv /path/to/venv/
pip install -r requirements.txt

WaveGAN generator

Word2Wave requires a pre-trained WaveGAN generator. In my experiments, I trained my own on the Freesound Loop Dataset, using this implementation. To download the FSL dataset do:

$ wget https://zenodo.org/record/3967852/files/FSL10K.zip?download=1

and then train following the instructions in the WaveGAN repo. Once trained, place the model in the wavegan folder:

๐Ÿ“‚wavegan
  โ”— ๐Ÿ“œgan_.tar

Pre-trained COALA encoders

You'll need to download the pre-trained weights for the COALA tag and audio encoders from the official repo. Note that the repo provides weights for the model trained with different configurations (e.g. different weights in the loss components). For more details on this, you can refer to the original code and paper. To download the model weights, you can run the following commands (or the equivalent for the desired model configuration)

$ wget https://raw.githubusercontent.com/xavierfav/coala/master/saved_models/dual_ae_c/audio_encoder_epoch_200.pt
$ wget https://raw.githubusercontent.com/xavierfav/coala/master/saved_models/dual_ae_c/tag_encoder_epoch_200.pt

Once downloaded, place them in the coala/models folder:

๐Ÿ“‚coala
 โ”ฃ ๐Ÿ“‚models
   โ”ฃ ๐Ÿ“‚dual_ae_c
     โ”ฃ ๐Ÿ“œaudio_encoder_epoch_200.pt
     โ”— ๐Ÿ“œtag_encoder_epoch_200.pt

How to use

For text-to-audio generation using the default parameters, simply do

$ python main.py "text prompt" --wavegan_path  --output_dir 

Citations

Some of the code in this repo is adapted from the official COALA repo and @mostafaelaraby's PyTorch implenentation of the WaveGAN model.

@inproceedings{donahue2018adversarial,
  title={Adversarial Audio Synthesis},
  author={Donahue, Chris and McAuley, Julian and Puckette, Miller},
  booktitle={International Conference on Learning Representations},
  year={2018}
}
@article{favory2020coala,
  title={Coala: Co-aligned autoencoders for learning semantically enriched audio representations},
  author={Favory, Xavier and Drossos, Konstantinos and Virtanen, Tuomas and Serra, Xavier},
  journal={arXiv preprint arXiv:2006.08386},
  year={2020}
}
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Comments
  • Colab notebook: Where are weights?

    Colab notebook: Where are weights?

    Thanks for sharing the notebook. Could it perhaps be documented a bit more to make it a bit easier for new users (like me) to make it work without crashing? I can't figure out how to fill in the missing information about where to find the weights.

    Below is a log of my run...

    !nvidia-smi -L
    GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-5218d88a-592a-b7c2-d10c-ff61031ab247)
    

    Mount your drive

    Mounted at /content/drive
    

    Install Word2Wave, import necessary packages

    Cloning into 'word2wave'...
    remote: Enumerating objects: 349, done.
    remote: Counting objects: 100% (349/349), done.
    remote: Compressing objects: 100% (311/311), done.
    remote: Total 349 (delta 185), reused 81 (delta 33), pack-reused 0
    Receiving objects: 100% (349/349), 1.10 MiB | 5.21 MiB/s, done.
    Resolving deltas: 100% (185/185), done.
    /content/word2wave
    Requirement already satisfied: matplotlib>=2.2.4 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 1)) (3.2.2)
    Requirement already satisfied: numpy>=1.16.3 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (1.19.5)
    Collecting librosa==0.6.3
      Downloading librosa-0.6.3.tar.gz (1.6 MB)
         |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1.6 MB 5.0 MB/s 
    Collecting pescador>=2.0.1
      Downloading pescador-2.1.0.tar.gz (20 kB)
    Requirement already satisfied: torch>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 5)) (1.10.0+cu111)
    Requirement already satisfied: tqdm>=4.32.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (4.62.3)
    Collecting numba==0.49.0
      Downloading numba-0.49.0-cp37-cp37m-manylinux2014_x86_64.whl (3.6 MB)
         |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 3.6 MB 36.1 MB/s 
    Collecting torchaudio==0.8.1
      Downloading torchaudio-0.8.1-cp37-cp37m-manylinux1_x86_64.whl (1.9 MB)
         |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1.9 MB 64.3 MB/s 
    Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.6.3->-r requirements.txt (line 3)) (2.1.9)
    Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.6.3->-r requirements.txt (line 3)) (1.4.1)
    Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.6.3->-r requirements.txt (line 3)) (1.0.1)
    Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.7/dist-packages (from librosa==0.6.3->-r requirements.txt (line 3)) (1.1.0)
    Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.6.3->-r requirements.txt (line 3)) (4.4.2)
    Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.7/dist-packages (from librosa==0.6.3->-r requirements.txt (line 3)) (1.15.0)
    Requirement already satisfied: resampy>=0.2.0 in /usr/local/lib/python3.7/dist-packages (from librosa==0.6.3->-r requirements.txt (line 3)) (0.2.2)
    Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba==0.49.0->-r requirements.txt (line 7)) (57.4.0)
    Collecting llvmlite<=0.33.0.dev0,>=0.31.0.dev0
      Downloading llvmlite-0.32.1-cp37-cp37m-manylinux1_x86_64.whl (20.2 MB)
         |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 20.2 MB 1.3 MB/s 
    Collecting torch>=1.1.0
      Downloading torch-1.8.1-cp37-cp37m-manylinux1_x86_64.whl (804.1 MB)
         |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 804.1 MB 2.6 kB/s 
    Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.1.0->-r requirements.txt (line 5)) (3.10.0.2)
    Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2.4->-r requirements.txt (line 1)) (2.8.2)
    Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2.4->-r requirements.txt (line 1)) (3.0.6)
    Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2.4->-r requirements.txt (line 1)) (1.3.2)
    Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2.4->-r requirements.txt (line 1)) (0.11.0)
    Requirement already satisfied: pyzmq>=15.0 in /usr/local/lib/python3.7/dist-packages (from pescador>=2.0.1->-r requirements.txt (line 4)) (22.3.0)
    Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn!=0.19.0,>=0.14.0->librosa==0.6.3->-r requirements.txt (line 3)) (3.0.0)
    Building wheels for collected packages: librosa, pescador
      Building wheel for librosa (setup.py) ... done
      Created wheel for librosa: filename=librosa-0.6.3-py3-none-any.whl size=1573336 sha256=fff7ac07e9d03aa008fcf3f1f369f8acffdb27082d75ffead993dfeb62fb468d
      Stored in directory: /root/.cache/pip/wheels/de/c1/94/619fb8b04ee1f567115662d26650677ecf79bc7d8e462d21f8
      Building wheel for pescador (setup.py) ... done
      Created wheel for pescador: filename=pescador-2.1.0-py3-none-any.whl size=21104 sha256=4a7aaeaff3c65a1913ee3bad1cbd83c1c6e541790056b38a2848a31f5568f4e9
      Stored in directory: /root/.cache/pip/wheels/f0/e3/c6/32d30d5eb5292dac352d2fca4ebf393aa94e09b9b8b4b0f341
    Successfully built librosa pescador
    Installing collected packages: llvmlite, numba, torch, torchaudio, pescador, librosa
      Attempting uninstall: llvmlite
        Found existing installation: llvmlite 0.34.0
        Uninstalling llvmlite-0.34.0:
          Successfully uninstalled llvmlite-0.34.0
      Attempting uninstall: numba
        Found existing installation: numba 0.51.2
        Uninstalling numba-0.51.2:
          Successfully uninstalled numba-0.51.2
      Attempting uninstall: torch
        Found existing installation: torch 1.10.0+cu111
        Uninstalling torch-1.10.0+cu111:
          Successfully uninstalled torch-1.10.0+cu111
      Attempting uninstall: torchaudio
        Found existing installation: torchaudio 0.10.0+cu111
        Uninstalling torchaudio-0.10.0+cu111:
          Successfully uninstalled torchaudio-0.10.0+cu111
      Attempting uninstall: librosa
        Found existing installation: librosa 0.8.1
        Uninstalling librosa-0.8.1:
          Successfully uninstalled librosa-0.8.1
    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
    torchvision 0.11.1+cu111 requires torch==1.10.0, but you have torch 1.8.1 which is incompatible.
    torchtext 0.11.0 requires torch==1.10.0, but you have torch 1.8.1 which is incompatible.
    kapre 0.3.6 requires librosa>=0.7.2, but you have librosa 0.6.3 which is incompatible.
    Successfully installed librosa-0.6.3 llvmlite-0.32.1 numba-0.49.0 pescador-2.1.0 torch-1.8.1 torchaudio-0.8.1
    /usr/local/lib/python3.7/dist-packages/librosa/util/decorators.py:9: NumbaDeprecationWarning: An import was requested from a module that has moved location.
    Import requested from: 'numba.decorators', please update to use 'numba.core.decorators' or pin to Numba version 0.48.0. This alias will not be present in Numba version 0.50.0.
      from numba.decorators import jit as optional_jit
    /usr/local/lib/python3.7/dist-packages/librosa/util/decorators.py:9: NumbaDeprecationWarning: An import was requested from a module that has moved location.
    Import of 'jit' requested from: 'numba.decorators', please update to use 'numba.core.decorators' or pin to Numba version 0.48.0. This alias will not be present in Numba version 0.50.0.
      from numba.decorators import jit as optional_jit
    

    But then part that says...

    Copy the pre-trained WaveGAN and COALA weights from drive

    drive_path:  "/content/drive/<path/to/word2wave_files/>" 
    

    ...it's not clear from the notebook what to enter in this string.

    I see further up in the output that it seems to have installed into /content/word2wave, but when I type that into the prompt, I get

    cp: cannot stat '/content/word2wavewavegan': No such file or directory
    cp: cannot stat '/content/word2wavecoala': No such file or directory
    mv: cannot stat '/content/word2wave/coala/coala/': No such file or directory
    

    Looking around on Drive, I see...

    !ls /content/drive
    
    MyDrive  Shareddrives
    

    If I just ignore the above errors and try to run the notebook with no changes, then when it comes to the part for generating with "firework", I see:

    NameError                                 Traceback (most recent call last)
    <ipython-input-9-cf00c4445550> in <module>()
          9 id2tag = json.load(open('/content/word2wave/coala/id2token_top_1000.json', 'rb'))
         10 
    ---> 11 check_text_input(text)
    
    <ipython-input-7-0cff9a5913d2> in check_text_input(text)
         28 
         29 def check_text_input(text):
    ---> 30   _, words_in_dict, words_not_in_dict = word2wave.tokenize_text(text)
         31   if not words_in_dict:
         32       raise Exception("All the words in the text prompt are out-of-vocabulary, please try with another prompt")
    
    NameError: name 'word2wave' is not defined
    
    opened by drscotthawley 4
  • train/valid splits used for FSL10K

    train/valid splits used for FSL10K

    It looks like the WaveGAN code in the wavegan-pytorch repo you used assumes that the audio files are split into train and valid subdirectories, but the FSL10K dataset doesn't seem to have any information about standard splits on their Zenodo page or in their paper. Do you have any information about the train/valid split you used?

    opened by ecooper7 0
  • Error with pip install -r requirements.txt

    Error with pip install -r requirements.txt

    I'm getting these errors with the command pip install -r requirements.txt

      ERROR: Command errored out with exit status 1:
       command: 'C:\Users\Computer\anaconda3\python.exe' -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\Computer\\AppData\\Local\\Temp\\pip-install-vqe1knc0\\llvmlite_e671eaa75a104e25a13f7826ddcf3a51\\setup.py'"'"'; __file__='"'"'C:\\Users\\Computer\\AppData\\Local\\Temp\\pip-install-vqe1knc0\\llvmlite_e671eaa75a104e25a13f7826ddcf3a51\\setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d 'C:\Users\Computer\AppData\Local\Temp\pip-wheel-a4bxxl_i'
           cwd: C:\Users\Computer\AppData\Local\Temp\pip-install-vqe1knc0\llvmlite_e671eaa75a104e25a13f7826ddcf3a51\
      Complete output (24 lines):
      running bdist_wheel
      C:\Users\Computer\anaconda3\python.exe C:\Users\Computer\AppData\Local\Temp\pip-install-vqe1knc0\llvmlite_e671eaa75a104e25a13f7826ddcf3a51\ffi\build.py
      Trying generator 'Visual Studio 14 2015 Win64'
      Traceback (most recent call last):
        File "C:\Users\Computer\AppData\Local\Temp\pip-install-vqe1knc0\llvmlite_e671eaa75a104e25a13f7826ddcf3a51\ffi\build.py", line 192, in <module>
          main()
        File "C:\Users\Computer\AppData\Local\Temp\pip-install-vqe1knc0\llvmlite_e671eaa75a104e25a13f7826ddcf3a51\ffi\build.py", line 180, in main
          main_win32()
        File "C:\Users\Computer\AppData\Local\Temp\pip-install-vqe1knc0\llvmlite_e671eaa75a104e25a13f7826ddcf3a51\ffi\build.py", line 89, in main_win32
          generator = find_win32_generator()
        File "C:\Users\Computer\AppData\Local\Temp\pip-install-vqe1knc0\llvmlite_e671eaa75a104e25a13f7826ddcf3a51\ffi\build.py", line 77, in find_win32_generator
          try_cmake(cmake_dir, build_dir, generator)
        File "C:\Users\Computer\AppData\Local\Temp\pip-install-vqe1knc0\llvmlite_e671eaa75a104e25a13f7826ddcf3a51\ffi\build.py", line 28, in try_cmake
          subprocess.check_call(['cmake', '-G', generator, cmake_dir])
        File "C:\Users\Computer\anaconda3\lib\subprocess.py", line 368, in check_call
          retcode = call(*popenargs, **kwargs)
        File "C:\Users\Computer\anaconda3\lib\subprocess.py", line 349, in call
          with Popen(*popenargs, **kwargs) as p:
        File "C:\Users\Computer\anaconda3\lib\subprocess.py", line 951, in __init__
          self._execute_child(args, executable, preexec_fn, close_fds,
        File "C:\Users\Computer\anaconda3\lib\subprocess.py", line 1420, in _execute_child
          hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
      FileNotFoundError: [WinError 2] The system cannot find the file specified
      error: command 'C:\\Users\\Computer\\anaconda3\\python.exe' failed with exit code 1
      ----------------------------------------
      ERROR: Failed building wheel for llvmlite
      Running setup.py clean for llvmlite
    Successfully built numba
    Failed to build llvmlite
    Installing collected packages: llvmlite, numba, torch, resampy, audioread, torchaudio, pescador, librosa
      Attempting uninstall: llvmlite
        Found existing installation: llvmlite 0.37.0
    ERROR: Cannot uninstall 'llvmlite'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.```
    opened by Redivh 0
Owner
Ilaria Manco
AI & Music PhD Researcher at the Centre for Digital Music (QMUL)
Ilaria Manco
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Python interface to Stanford Core NLP tools v3.4.1 This is a Python wrapper for Stanford University's NLP group's Java-based CoreNLP tools. It can eit

Dustin Smith 610 Sep 07, 2022
Implementation of TTS with combination of Tacotron2 and HiFi-GAN

Tacotron2-HiFiGAN-master Implementation of TTS with combination of Tacotron2 and HiFi-GAN for Mandarin TTS. Inference In order to inference, we need t

SunLu Z 7 Nov 11, 2022
Maha is a text processing library specially developed to deal with Arabic text.

An Arabic text processing library intended for use in NLP applications Maha is a text processing library specially developed to deal with Arabic text.

Mohammad Al-Fetyani 184 Nov 27, 2022
A python script that will use hydra to get user and password to login to ssh, ftp, and telnet

Hydra-Auto-Hack A python script that will use hydra to get user and password to login to ssh, ftp, and telnet Project Description This python script w

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Code for EMNLP20 paper: "ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training"

ProphetNet-X This repo provides the code for reproducing the experiments in ProphetNet. In the paper, we propose a new pre-trained language model call

Microsoft 394 Dec 17, 2022
A pytorch implementation of the ACL2019 paper "Simple and Effective Text Matching with Richer Alignment Features".

RE2 This is a pytorch implementation of the ACL 2019 paper "Simple and Effective Text Matching with Richer Alignment Features". The original Tensorflo

286 Jan 02, 2023
Natural Language Processing library built with AllenNLP ๐ŸŒฒ๐ŸŒฑ

Custom Natural Language Processing with big and small models ๐ŸŒฒ๐ŸŒฑ

Recognai 65 Sep 13, 2022
This is the Alpha of Nutte language, she is not complete yet / Essa รฉ a Alpha da Nutte language, nรฃo estรก completa ainda

nutte-language This is the Alpha of Nutte language, it is not complete yet / Essa รฉ a Alpha da Nutte language, nรฃo estรก completa ainda My language was

catdochrome 2 Dec 18, 2021
Document processing using transformers

Doc Transformers Document processing using transformers. This is still in developmental phase, currently supports only extraction of form data i.e (ke

Vishnu Nandakumar 13 Dec 21, 2022
ๆœฌ้กน็›ฎๆ˜ฏไฝœ่€…ไปฌๆ นๆฎไธชไบบ้ข่ฏ•ๅ’Œ็ป้ชŒๆ€ป็ป“ๅ‡บ็š„่‡ช็„ถ่ฏญ่จ€ๅค„็†(NLP)้ข่ฏ•ๅ‡†ๅค‡็š„ๅญฆไน ็ฌ”่ฎฐไธŽ่ต„ๆ–™๏ผŒ่ฏฅ่ต„ๆ–™็›ฎๅ‰ๅŒ…ๅซ ่‡ช็„ถ่ฏญ่จ€ๅค„็†ๅ„้ข†ๅŸŸ็š„ ้ข่ฏ•้ข˜็งฏ็ดฏใ€‚

ใ€ๅ…ณไบŽ NLPใ€‘้‚ฃไบ›ไฝ ไธ็Ÿฅ้“็š„ไบ‹ ไฝœ่€…๏ผšๆจๅค•ใ€่Š™่•–ใ€ๆŽ็Žฒใ€้™ˆๆตท้กบใ€twilightใ€LeoLRHใ€JimmyDUใ€่‰พๆ˜ฅ่พ‰ใ€ๅผ ๆฐธๆณฐใ€้‡‘้‡‘้‡‘ ไป‹็ป ๆœฌ้กน็›ฎๆ˜ฏไฝœ่€…ไปฌๆ นๆฎไธชไบบ้ข่ฏ•ๅ’Œ็ป้ชŒๆ€ป็ป“ๅ‡บ็š„่‡ช็„ถ่ฏญ่จ€ๅค„็†(NLP)้ข่ฏ•ๅ‡†ๅค‡็š„ๅญฆไน ็ฌ”่ฎฐไธŽ่ต„ๆ–™๏ผŒ่ฏฅ่ต„ๆ–™็›ฎๅ‰ๅŒ…ๅซ ่‡ช็„ถ่ฏญ่จ€ๅค„็†ๅ„้ข†ๅŸŸ็š„ ้ข่ฏ•้ข˜็งฏ็ดฏใ€‚ ็›ฎๅฝ•ๆžถๆž„ ไธ€ใ€ใ€

1.4k Dec 30, 2022