PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

Overview

StyleSpeech - PyTorch Implementation

PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation.

Status (2021.06.09)

  • StyleSpeech
  • Meta-StyleSpeech

Quickstart

Dependencies

You can install the Python dependencies with

pip3 install -r requirements.txt

Inference

You have to download the pretrained models and put them in output/ckpt/LibriTTS/.

For English single-speaker TTS, run

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --ref_audio path/to/reference_audio.wav --speaker_id <SPEAKER_ID> --restore_step 100000 --mode single -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml

The generated utterances will be put in output/result/. Your synthesized speech will have ref_audio's style spoken by speaker_id speaker. Note that the controllability of speakers is not a vital interest of StyleSpeech.

Batch Inference

Batch inference is also supported, try

python3 synthesize.py --source preprocessed_data/LibriTTS/val.txt --restore_step 100000 --mode batch -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml

to synthesize all utterances in preprocessed_data/LibriTTS/val.txt. This can be viewed as a reconstruction of validation datasets referring to themselves for the reference style.

Controllability

The pitch/volume/speaking rate of the synthesized utterances can be controlled by specifying the desired pitch/energy/duration ratios. For example, one can increase the speaking rate by 20 % and decrease the volume by 20 % by

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --restore_step 100000 --mode single -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml --duration_control 0.8 --energy_control 0.8

Note that the controllability is originated from FastSpeech2 and not a vital interest of StyleSpeech.

Training

Datasets

The supported datasets are

  • LibriTTS: a multi-speaker English dataset containing 585 hours of speech by 2456 speakers.
  • (will be added more)

Preprocessing

First, run

python3 prepare_align.py config/LibriTTS/preprocess.yaml

for some preparations.

In this implementation, Montreal Forced Aligner (MFA) is used to obtain the alignments between the utterances and the phoneme sequences.

Download the official MFA package and run

./montreal-forced-aligner/bin/mfa_align raw_data/LibriTTS/ lexicon/librispeech-lexicon.txt english preprocessed_data/LibriTTS

or

./montreal-forced-aligner/bin/mfa_train_and_align raw_data/LibriTTS/ lexicon/librispeech-lexicon.txt preprocessed_data/LibriTTS

to align the corpus and then run the preprocessing script.

python3 preprocess.py config/LibriTTS/preprocess.yaml

Training

Train your model with

python3 train.py -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml

TensorBoard

Use

tensorboard --logdir output/log/LibriTTS

to serve TensorBoard on your localhost. The loss curves, synthesized mel-spectrograms, and audios are shown.

Implementation Issues

  1. Use 22050Hz sampling rate instead of 16kHz.
  2. Add one fully connected layer at the beginning of Mel-Style Encoder to upsample input mel-spectrogram from 80 to 128.
  3. The Paper doesn't mention speaker embedding for the Generator, but I add it as a normal multi-speaker TTS. And the style_prototype of Meta-StyleSpeech can be seen as a speaker embedding space.
  4. Use HiFi-GAN instead of MelGAN for vocoding.

Citation

@misc{lee2021stylespeech,
  author = {Lee, Keon},
  title = {StyleSpeech},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/keonlee9420/StyleSpeech}}
}

References

Comments
  • What is the perfermance compared with Adaspeech

    What is the perfermance compared with Adaspeech

    Thank you for your great work and share. Your work looks differ form adaspeech and NAUTILUS. You use GANs which i did not see in other papers regarding adaptative TTS. Have you compare this method with adaspeech1/2? how about the mos and similarity?

    opened by Liujingxiu23 10
  • The size of tensor a (xx) must match the size of tensor b (yy)

    The size of tensor a (xx) must match the size of tensor b (yy)

    Hi I try to run your project. I use cuda 10.1, all requirements are installed (with torch 1.8.1), all models are preloaded. But i have an error: python3 synthesize.py --text "Hello world" --restore_step 200000 --mode single -p config/LibriTTS/preprocess.yaml -m config/LibriTTS/model.yaml -t config/LibriTTS/train.yaml --duration_control 0.8 --energy_control 0.8 --ref_audio ref.wav

    Removing weight norm...
    Raw Text Sequence: Hello world
    Phoneme Sequence: {HH AH0 L OW1 W ER1 L D}
    Traceback (most recent call last):
      File "synthesize.py", line 268, in <module>
        synthesize(model, args.restore_step, configs, vocoder, batchs, control_values)
      File "synthesize.py", line 152, in synthesize
        d_control=duration_control
      File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 889, in _call_impl
        result = self.forward(input, *kwargs)
      File "/usr/local/work/model/StyleSpeech.py", line 144, in forward
        d_control,
      File "/usr/local/work/model/StyleSpeech.py", line 91, in G
        output, mel_masks = self.mel_decoder(output, style_vector, mel_masks)
      File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 889, in _call_impl
        result = self.forward(input, kwargs)
      File "/usr/local/work/model/modules.py", line 307, in forward
        enc_seq = self.mel_prenet(enc_seq, mask)
      File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 889, in _call_impl
        result = self.forward(input, kwargs)
      File "/usr/local/work/model/modules.py", line 259, in forward
        x = x.masked_fill(mask.unsqueeze(-1), 0)
    RuntimeError: The size of tensor a (44) must match the size of tensor b (47) at non-singleton dimension 1
    
    opened by DiDimus 9
  • VCTK datasets

    VCTK datasets

    Hi, I note your paper evaluates the models' performance on VCTK datasets, but I not see the process file about VCTK. Hence, could you share the files, thank you very much.

    opened by XXXHUA 7
  • training error

    training error

    Thanks for your sharing!

    I tried both naive and main branches using your checkpoints, it seems the former one is much better. So I trained AISHELL3 models with small changes on your code and the synthesized waves are good for me.

    However when I add my own data into AISHELL3, some error occurred: Training: 0%| | 3105/900000 [32:05<154:31:49, 1.61it/s] Epoch 2: 69%|██████████████████████▏ | 318/459 [05:02<02:14, 1.05it/s] File "train.py", line 211, in main(args, configs) File "train.py", line 87, in main output = model(*(batch[2:])) File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/opt/conda/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 165, in forward return self.module(*inputs[0], **kwargs[0]) File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/workspace/StyleSpeech-naive/model/StyleSpeech.py", line 83, in forward ) = self.variance_adaptor( File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/workspace/StyleSpeech-naive/model/modules.py", line 404, in forward x = x + pitch_embedding RuntimeError: The size of tensor a (52) must match the size of tensor b (53) at non-singleton dimension 1

    I only replaced two speakers and preprocessed data the same as the in readme.

    Do you have any advice for this error ? Any suggestion is appreciated.

    opened by MingZJU 6
  • the synthesis result is bad when using pretrain model

    the synthesis result is bad when using pretrain model

    hello sir, thanks for your sharing.

    i meet a problem when i using pretrain model to synthsize demo file. the effect of synthesized wav is so bad.

    do you konw what problem happened?

    pretrain_model: output/ckpt/LibriTTS_meta_learner/200000.pth.tar ref_audio: ref_audio.zip demo_txt: {Promises are often like the butterfly, which disappear after beautiful hover. No matter the ending is perfect or not, you cannot disappear from my world.} demo_wav:demo.zip

    opened by mnfutao 4
  • Maybe style_prototype can instead of ref_mel?

    Maybe style_prototype can instead of ref_mel?

    hello @keonlee9420 , thanks for your contribution on StyleSpeech. When I read your paper and source code, I think that the style_prototype (which is an embedding matrix) maybe can instread of the ref_mel, because there is a CE-loss between style_prototype and style_vector, which can control this embedding matrix close to style. In short, we can give a speaker id to synthesize this speaker's wave. Is it right?

    opened by forwiat 3
  • architecture shows bad results

    architecture shows bad results

    Hi, i have completely repeated your steps for learning. During training, style speech loss fell down, but after learning began, meta style speech loss began to grow up. Can you help with training the model? I can describe my steps in more detail.

    opened by e0xextazy 2
  • UnboundLocalError: local variable 'pitch' referenced before assignment

    UnboundLocalError: local variable 'pitch' referenced before assignment

    Hi, when I run preprocessor.py, I have this problem: /preprocessor.py", line 92, in build_from_path if len(pitch) > 0: UnboundLocalError: local variable 'pitch' referenced before assignment When I try to add a global declaration to the function, it shows NameError: name 'pitch' is not defined How should this be resolved? I would be grateful if I could get your guidance soon.

    opened by Summerxu86 0
  • How can I improve the synthesized results?

    How can I improve the synthesized results?

    I have trained the model for 200k steps, and still, the synthesised results are extremely bad. loss_curve This is what my loss curve looks like. Can you help me with what can I do now to improve my synthesized audio results?

    opened by sanjeevani279 1
  • RuntimeError: Error(s) in loading state_dict for Stylespeech

    RuntimeError: Error(s) in loading state_dict for Stylespeech

    Hi @keonlee9420, I am getting the following error, while running the naive branch :

    Traceback (most recent call last):
      File "synthesize.py", line 242, in <module>
        model = get_model(args, configs, device, train=False)
      File "/home/azureuser/aditya_workspace/stylespeech_keonlee_naive/utils/model.py", line 21, in get_model
        model.load_state_dict(ckpt["model"], strict=True)
      File "/home/azureuser/aditya_workspace/keonlee/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1223, in load_state_dict
        raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
    RuntimeError: Error(s) in loading state_dict for StyleSpeech:
    	Missing key(s) in state_dict: "D_t.mel_linear.0.fc_layer.fc_layer.linear.weight_orig", "D_t.mel_linear.0.fc_layer.fc_layer.linear.weight", "D_t.mel_linear.0.fc_layer.fc_layer.linear.weight_u", "D_t.mel_linear.0.fc_layer.fc_layer.linear.weight_orig", "D_t.mel_linear.0.fc_layer.fc_layer.linear.weight_u", "D_t.mel_linear.0.fc_layer.fc_layer.linear.weight_v", "D_t.mel_linear.1.fc_layer.fc_layer.linear.weight_orig", "D_t.mel_linear.1.fc_layer.fc_layer.linear.weight", "D_t.mel_linear.1.fc_layer.fc_layer.linear.weight_u", "D_t.mel_linear.1.fc_layer.fc_layer.linear.weight_orig", "D_t.mel_linear.1.fc_layer.fc_layer.linear.weight_u", "D_t.mel_linear.1.fc_layer.fc_layer.linear.weight_v", "D_t.discriminator_stack.0.fc_layer.fc_layer.linear.weight_orig", "D_t.discriminator_stack.0.fc_layer.fc_layer.linear.weight", "D_t.discriminator_stack.0.fc_layer.fc_layer.linear.weight_u", "D_t.discriminator_stack.0.fc_layer.fc_layer.linear.weight_orig", "D_t.discriminator_stack.0.fc_layer.fc_layer.linear.weight_u", "D_t.discriminator_stack.0.fc_layer.fc_layer.linear.weight_v", "D_t.discriminator_stack.1.fc_layer.fc_layer.linear.weight_orig", "D_t.discriminator_stack.1.fc_layer.fc_layer.linear.weight", "D_t.discriminator_stack.1.fc_layer.fc_layer.linear.weight_u", "D_t.discriminator_stack.1.fc_layer.fc_layer.linear.weight_orig", "D_t.discriminator_stack.1.fc_layer.fc_layer.linear.weight_u", "D_t.discriminator_stack.1.fc_layer.fc_layer.linear.weight_v", "D_t.discriminator_stack.2.fc_layer.fc_layer.linear.weight_orig", "D_t.discriminator_stack.2.fc_layer.fc_layer.linear.weight", "D_t.discriminator_stack.2.fc_layer.fc_layer.linear.weight_u", "D_t.discriminator_stack.2.fc_layer.fc_layer.linear.weight_orig", "D_t.discriminator_stack.2.fc_layer.fc_layer.linear.weight_u", "D_t.discriminator_stack.2.fc_layer.fc_layer.linear.weight_v", "D_t.final_linear.fc_layer.fc_layer.linear.weight_orig", "D_t.final_linear.fc_layer.fc_layer.linear.weight", "D_t.final_linear.fc_layer.fc_layer.linear.weight_u", "D_t.final_linear.fc_layer.fc_layer.linear.weight_orig", "D_t.final_linear.fc_layer.fc_layer.linear.weight_u", "D_t.final_linear.fc_layer.fc_layer.linear.weight_v", "D_s.fc_1.fc_layer.fc_layer.linear.weight_orig", "D_s.fc_1.fc_layer.fc_layer.linear.weight", "D_s.fc_1.fc_layer.fc_layer.linear.weight_u", "D_s.fc_1.fc_layer.fc_layer.linear.weight_orig", "D_s.fc_1.fc_layer.fc_layer.linear.weight_u", "D_s.fc_1.fc_layer.fc_layer.linear.weight_v", "D_s.spectral_stack.0.fc_layer.fc_layer.linear.weight_orig", "D_s.spectral_stack.0.fc_layer.fc_layer.linear.weight", "D_s.spectral_stack.0.fc_layer.fc_layer.linear.weight_u", "D_s.spectral_stack.0.fc_layer.fc_layer.linear.weight_orig", "D_s.spectral_stack.0.fc_layer.fc_layer.linear.weight_u", "D_s.spectral_stack.0.fc_layer.fc_layer.linear.weight_v", "D_s.spectral_stack.1.fc_layer.fc_layer.linear.weight_orig", "D_s.spectral_stack.1.fc_layer.fc_layer.linear.weight", "D_s.spectral_stack.1.fc_layer.fc_layer.linear.weight_u", "D_s.spectral_stack.1.fc_layer.fc_layer.linear.weight_orig", "D_s.spectral_stack.1.fc_layer.fc_layer.linear.weight_u", "D_s.spectral_stack.1.fc_layer.fc_layer.linear.weight_v", "D_s.temporal_stack.0.conv_layer.conv_layer.conv.weight_orig", "D_s.temporal_stack.0.conv_layer.conv_layer.conv.weight", "D_s.temporal_stack.0.conv_layer.conv_layer.conv.weight_u", "D_s.temporal_stack.0.conv_layer.conv_layer.conv.bias", "D_s.temporal_stack.0.conv_layer.conv_layer.conv.weight_orig", "D_s.temporal_stack.0.conv_layer.conv_layer.conv.weight_u", "D_s.temporal_stack.0.conv_layer.conv_layer.conv.weight_v", "D_s.temporal_stack.1.conv_layer.conv_layer.conv.weight_orig", "D_s.temporal_stack.1.conv_layer.conv_layer.conv.weight", "D_s.temporal_stack.1.conv_layer.conv_layer.conv.weight_u", "D_s.temporal_stack.1.conv_layer.conv_layer.conv.bias", "D_s.temporal_stack.1.conv_layer.conv_layer.conv.weight_orig", "D_s.temporal_stack.1.conv_layer.conv_layer.conv.weight_u", "D_s.temporal_stack.1.conv_layer.conv_layer.conv.weight_v", "D_s.slf_attn_stack.0.w_qs.linear.weight_orig", "D_s.slf_attn_stack.0.w_qs.linear.weight", "D_s.slf_attn_stack.0.w_qs.linear.weight_u", "D_s.slf_attn_stack.0.w_qs.linear.weight_orig", "D_s.slf_attn_stack.0.w_qs.linear.weight_u", "D_s.slf_attn_stack.0.w_qs.linear.weight_v", "D_s.slf_attn_stack.0.w_ks.linear.weight_orig", "D_s.slf_attn_stack.0.w_ks.linear.weight", "D_s.slf_attn_stack.0.w_ks.linear.weight_u", "D_s.slf_attn_stack.0.w_ks.linear.weight_orig", "D_s.slf_attn_stack.0.w_ks.linear.weight_u", "D_s.slf_attn_stack.0.w_ks.linear.weight_v", "D_s.slf_attn_stack.0.w_vs.linear.weight_orig", "D_s.slf_attn_stack.0.w_vs.linear.weight", "D_s.slf_attn_stack.0.w_vs.linear.weight_u", "D_s.slf_attn_stack.0.w_vs.linear.weight_orig", "D_s.slf_attn_stack.0.w_vs.linear.weight_u", "D_s.slf_attn_stack.0.w_vs.linear.weight_v", "D_s.slf_attn_stack.0.layer_norm.weight", "D_s.slf_attn_stack.0.layer_norm.bias", "D_s.slf_attn_stack.0.fc.linear.weight_orig", "D_s.slf_attn_stack.0.fc.linear.weight", "D_s.slf_attn_stack.0.fc.linear.weight_u", "D_s.slf_attn_stack.0.fc.linear.weight_orig", "D_s.slf_attn_stack.0.fc.linear.weight_u", "D_s.slf_attn_stack.0.fc.linear.weight_v", "D_s.fc_2.fc_layer.fc_layer.linear.weight_orig", "D_s.fc_2.fc_layer.fc_layer.linear.weight", "D_s.fc_2.fc_layer.fc_layer.linear.weight_u", "D_s.fc_2.fc_layer.fc_layer.linear.weight_orig", "D_s.fc_2.fc_layer.fc_layer.linear.weight_u", "D_s.fc_2.fc_layer.fc_layer.linear.weight_v", "D_s.V.fc_layer.fc_layer.linear.weight", "D_s.w_b_0.fc_layer.fc_layer.linear.weight", "D_s.w_b_0.fc_layer.fc_layer.linear.bias", "style_prototype.weight".
    	Unexpected key(s) in state_dict: "speaker_emb.weight".
    

    Can you help with this, seems like the pre-trained weights are old and do not conform to the current architecture.

    opened by sirius0503 1
  • time dimension doesn't match

    time dimension doesn't match

    ^MTraining: 0%| | 0/200000 [00:00<?, ?it/s] ^MEpoch 1: 0%| | 0/454 [00:00<?, ?it/s]^[[APrepare training ... Number of StyleSpeech Parameters: 28197333 Removing weight norm... Traceback (most recent call last): File "train.py", line 224, in main(args, configs) File "train.py", line 98, in main output = (None, None, model((batch[2:-5]))) File "/share/mini1/sw/std/python/anaconda3-2019.07/v3.7/envs/StyleSpeech/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/share/mini1/sw/std/python/anaconda3-2019.07/v3.7/envs/StyleSpeech/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 165, in forward return self.module(*inputs[0], **kwargs[0]) File "/share/mini1/sw/std/python/anaconda3-2019.07/v3.7/envs/StyleSpeech/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/share/mini1/res/t/vc/studio/timap-en/libritts/StyleSpeech/model/StyleSpeech.py", line 144, in forward d_control, File "/share/mini1/res/t/vc/studio/timap-en/libritts/StyleSpeech/model/StyleSpeech.py", line 88, in G d_control, File "/share/mini1/sw/std/python/anaconda3-2019.07/v3.7/envs/StyleSpeech/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/share/mini1/res/t/vc/studio/timap-en/libritts/StyleSpeech/model/modules.py", line 417, in forward x = x + pitch_embedding RuntimeError: The size of tensor a (132) must match the size of tensor b (130) at non-singleton dimension 1 ^MTraining: 0%| | 1/200000 [00:02<166:02:12, 2.99s/it]

    I think it might because of mfa I used. As mentioned in https://montreal-forced-aligner.readthedocs.io/en/latest/getting_started.html, I installed mfa through conda.

    Then I used mfa align raw_data/LibriTTS lexicon/librispeech-lexicon.txt english preprocessed_data/LibriTTS instead of the way you showed. But I can't find a way to run it as the way you showed, because I installed mfa through conda.

    opened by MingjieChen 24
Releases(v1.0.2)
Owner
Keon Lee
Expressive Speech Synthesis | Conversational AI | Open-domain Dialog | NLP | Generative Models | Empathic Computing | HCI
Keon Lee
Various Algorithms for Short Text Mining

Short Text Mining in Python Introduction This package shorttext is a Python package that facilitates supervised and unsupervised learning for short te

Kwan-Yuet 466 Dec 06, 2022
Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 models for speech recognition

Wav2Vec2 STT Python Beta Software Simple Python library, distributed via binary wheels with few direct dependencies, for easily using wav2vec 2.0 mode

David Zurow 22 Dec 29, 2022
Multilingual text (NLP) processing toolkit

polyglot Polyglot is a natural language pipeline that supports massive multilingual applications. Free software: GPLv3 license Documentation: http://p

RAMI ALRFOU 2.1k Jan 07, 2023
Official PyTorch implementation of Time-aware Large Kernel (TaLK) Convolutions (ICML 2020)

Time-aware Large Kernel (TaLK) Convolutions (Lioutas et al., 2020) This repository contains the source code, pre-trained models, as well as instructio

Vasileios Lioutas 28 Dec 07, 2022
Persian Bert For Long-Range Sequences

ParsBigBird: Persian Bert For Long-Range Sequences The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many ta

Sajjad Ayoubi 63 Dec 14, 2022
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0 🤗 Transformers provides thousands of pretrained models to perform tasks o

Hugging Face 77.3k Jan 03, 2023
The swas programming language

The Swas programming language This is a language that was made for fun. Installation Step 0: Make sure you have python installed Step 1. Clone this re

Swas.py 19 Jul 18, 2022
T‘rex Park is a Youzan sponsored project. Offering Chinese NLP and image models pretrained from E-commerce datasets

T‘rex Park is a Youzan sponsored project. Offering Chinese NLP and image models pretrained from E-commerce datasets (product titles, images, comments, etc.).

55 Nov 22, 2022
Training open neural machine translation models

Train Opus-MT models This package includes scripts for training NMT models using MarianNMT and OPUS data for OPUS-MT. More details are given in the Ma

Language Technology at the University of Helsinki 167 Jan 03, 2023
🌐 Translation microservice powered by AI

Dot Translate 🌐 A microservice for quick and local translation using A.I. This service starts a local webserver used for neural machine translation.

Dot HQ 48 Nov 22, 2022
Download videos from YouTube/Twitch/Twitter right in the Windows Explorer, without installing any shady shareware apps

youtube-dl and ffmpeg Windows Explorer Integration Download videos from YouTube/Twitch/Twitter and more (any platform that is supported by youtube-dl)

Wolfgang 226 Dec 30, 2022
A Japanese tokenizer based on recurrent neural networks

Nagisa is a python module for Japanese word segmentation/POS-tagging. It is designed to be a simple and easy-to-use tool. This tool has the following

325 Jan 05, 2023
Pretty-doc - Composable text objects with python

pretty-doc from __future__ import annotations from dataclasses import dataclass

Taine Zhao 2 Jan 17, 2022
Translators - is a library which aims to bring free, multiple, enjoyable translation to individuals and students in Python

Translators - is a library which aims to bring free, multiple, enjoyable translation to individuals and students in Python

UlionTse 907 Dec 27, 2022
A method for cleaning and classifying text using transformers.

NLP Translation and Classification The repository contains a method for classifying and cleaning text using NLP transformers. Overview The input data

Ray Chamidullin 0 Nov 15, 2022
Pretrained Japanese BERT models

Pretrained Japanese BERT models This is a repository of pretrained Japanese BERT models. The models are available in Transformers by Hugging Face. Mod

Inui Laboratory 387 Dec 30, 2022
A music comments dataset, containing 39,051 comments for 27,384 songs.

Music Comments Dataset A music comments dataset, containing 39,051 comments for 27,384 songs. For academic research use only. Introduction This datase

Zhang Yixiao 2 Jan 10, 2022
Contains analysis of trends from Fitbit Dataset (source: Kaggle) to see how the trends can be applied to Bellabeat customers and Bellabeat products

Contains analysis of trends from Fitbit Dataset (source: Kaggle) to see how the trends can be applied to Bellabeat customers and Bellabeat products.

Leah Pathan Khan 2 Jan 12, 2022
🚀Clone a voice in 5 seconds to generate arbitrary speech in real-time

English | 中文 Features 🌍 Chinese supported mandarin and tested with multiple datasets: aidatatang_200zh, magicdata, aishell3, data_aishell, and etc. ?

Vega 25.6k Dec 31, 2022
Transformer training code for sequential tasks

Sequential Transformer This is a code for training Transformers on sequential tasks such as language modeling. Unlike the original Transformer archite

Meta Research 578 Dec 13, 2022