coqui-tts

History

Eren Gölge 946afa8197 v0.8.0 (#1810 ) * Fix checkpointing GAN models (#1641) * checkpoint sae step crash fix * checkpoint save step crash fix * Update gan.py updated requested changes * crash fix * Fix the --model_name and --vocoder_name arguments need a <model_type> element (#1469) Co-authored-by: Eren Gölge <erogol@hotmail.com> * Fix Publish CI (#1597) * Try out manylinux * temporary removal of useless pipeline * remove check and use only manylinux * Try --plat-name * Add install requirements * Add back other actions * Add PR trigger * Remove conditions * Fix sythax * Roll back some changes * Add other python versions * Add test pypi upload * Add username * Add back __token__ as username * Modify name of entry to testpypi * Set it to release only * Fix version checking * Fix tokenizer for punc only (#1717) * Remove redundant config field * Fix SSIM loss * Separate loss tests * Fix BCELoss adressing #1192 * Make style * Add durations as aux input for VITS (#1694) * Add durations as aux input for VITS * Make style * Fix tts_tests * Fix test_get_aux_input * Make lint * feat: updated recipes and lr fix (#1718) - updated the recipes activating more losses for more stable training - re-enabling guided attention loss - fixed a bug about not the correct lr fetched for logging * Implement VitsAudioConfig (#1556) * Implement VitsAudioConfig * Update VITS LJSpeech recipe * Update VITS VCTK recipe * Make style * Add missing decorator * Add missing param * Make style * Update recipes * Fix test * Bug fix * Exclude tests folder * Make linter * Make style * Fix device allocation * Fix SSIM loss correction * Fix aux tests (#1753) * Set n_jobs to 1 for resample script * Delete resample test * Set n_jobs 1 in vad test * delete vad test * Revert "Delete resample test" This reverts commit `bb7c8466af`. * Remove tests with resample * Fix for FloorDiv Function Warning (#1760) * Fix for Floor Function Warning Fix for Floor Function Warning * Adding double quotes to fix formatting Adding double quotes to fix formatting * Update glow_tts.py * Update glow_tts.py * Fix type in download_vctk.sh (#1739) typo in comment * Update decoder.py (#1792) Minor comment correction. * Update requirements.txt (#1791) Support for #1775 * Update README.md (#1776) Fix typo in different and code sample * Fix & update WaveRNN vocoder model (#1749) * Fixes KeyError bug. Adding logging to dashboard. * Make pep8 compliant * Make style compliant * Still fixing style * Fix rand_segment edge case (input_len == seg_len - 1) * Update requirements.txt; inflect==5.6 (#1809) New inflect version (6.0) depends on pydantic which has some issues irrelevant to 🐸 TTS. #1808 Force inflect==5.6 (pydantic free) install to solve dependency issue. * Update README.md; download progress bar in CLI. (#1797) * Update README.md - minor PR - added model_info usage guide based on #1623 in README.md . * "added tqdm bar for model download" * Update manage.py * fixed style * fixed style * sort imports * Update wavenet.py (#1796) * Update wavenet.py Current version does not use "in_channels" argument. In glowTTS, we use normalizing flows and so "input dim" == "ouput dim" (channels and length). So, the existing code just uses hidden_channel sized tensor as input to first layer as well as outputs hidden_channel sized tensor. However, since it is a generic implementation, I believe it is better to update it for a more general use. * "in_channels -> hidden_channels" * Adjust default to be able to process longer sentences (#1835) Running `tts --text "$text" --out_path …` with a somewhat longer sentences in the text will lead to warnings like “Decoder stopped with max_decoder_steps 500” and the sentences just being cut off in the resulting WAV file. This happens quite frequently when feeding longer texts (e.g. a blog post) to `tts`. It's particular frustrating since the error is not always obvious in the output. You have to notice that there are missing parts. This is something other users seem to have run into as well [1]. This patch simply increases the maximum number of steps allowed for the tacotron decoder to fix this issue, resulting in a smoother default behavior. [1] https://github.com/mozilla/TTS/issues/734 * Fix language flags generated by espeak-ng phonemizer (#1801) * fix language flags generated by espeak-ng phonemizer * Style * Updated language flag regex to consider all language codes alike * fix get_random_embeddings --> get_random_embedding (#1726) * fix get_random_embeddings --> get_random_embedding function typo leads to training crash, no such function * fix typo get_random_embedding * Introduce numpy and torch transforms (#1705) * Refactor audio processing functions * Add tests for numpy transforms * Fix imports * Fix imports2 * Implement bucketed weighted sampling for VITS (#1871) * Update capacitron_layers.py (#1664) crashing because of dimension miss match at line no. 57 [batch, 256] vs [batch , 1, 512] enc_out = torch.cat([enc_out, speaker_embedding], dim=-1) * updates to dataset analysis notebooks for compatibility with latest version of TTS (#1853) * Fix BCE loss issue (#1872) * Fix BCE loss issue * Remove import * Remove deprecated files (#1873) - samplers.py is moved - distribute.py is replaces by the 👟Trainer * Handle when no batch sampler (#1882) * Fix tune wavegrad (#1844) * fix imports in tune_wavegrad * load_config returns Coqpit object instead None * set action (store true) for flag "--use_cuda"; start to tune if module is running as the main program * fix var order in the result of batch collating * make style * make style with black and isort * Bump up to v0.8.0 * Add new DE Thorsten models (#1898) - Tacotron2-DDC - HifiGAN vocoder Co-authored-by: manmay nakhashi <manmay.nakhashi@gmail.com> Co-authored-by: camillem <camillem@users.noreply.github.com> Co-authored-by: WeberJulian <julian.weber@hotmail.fr> Co-authored-by: a-froghyar <adamfroghyar@gmail.com> Co-authored-by: ivan provalov <iprovalo@yahoo.com> Co-authored-by: Tsai Meng-Ting <sarah13680@gmail.com> Co-authored-by: p0p4k <rajiv.punmiya@gmail.com> Co-authored-by: Yuri Pourre <yuripourre@users.noreply.github.com> Co-authored-by: vanIvan <alfa1211@gmail.com> Co-authored-by: Lars Kiesow <lkiesow@uos.de> Co-authored-by: rbaraglia <baraglia.r@live.fr> Co-authored-by: jchai.me <jreus@users.noreply.github.com> Co-authored-by: Stanislav Kachnov <42406556+geth-network@users.noreply.github.com>		2022-08-22 14:54:38 +02:00
..
configs	Make style (#1405 )	2022-03-16 12:13:55 +01:00
models	v0.8.0 (#1810 )	2022-08-22 14:54:38 +02:00
utils	v0.8.0 (#1810 )	2022-08-22 14:54:38 +02:00
README.md	Update model file extension (#1422 )	2022-03-22 17:55:00 +01:00
__init__.py	REBASED: Transform Speaker Encoder in a Generic Encoder and Implement Emotion Encoder training support (#1349 )	2022-03-11 14:43:40 +01:00
dataset.py	Make style (#1405 )	2022-03-16 12:13:55 +01:00
losses.py	Make style (#1405 )	2022-03-16 12:13:55 +01:00
requirements.txt	REBASED: Transform Speaker Encoder in a Generic Encoder and Implement Emotion Encoder training support (#1349 )	2022-03-11 14:43:40 +01:00

README.md

Speaker Encoder

This is an implementation of https://arxiv.org/abs/1710.10467. This model can be used for voice and speaker embedding.

With the code here you can generate d-vectors for both multi-speaker and single-speaker TTS datasets, then visualise and explore them along with the associated audio files in an interactive chart.

Below is an example showing embedding results of various speakers. You can generate the same plot with the provided notebook as demonstrated in this video.

Download a pretrained model from Released Models page.

To run the code, you need to follow the same flow as in TTS.

Define 'config.json' for your needs. Note that, audio parameters should match your TTS model.
Example training call python speaker_encoder/train.py --config_path speaker_encoder/config.json --data_path ~/Data/Libri-TTS/train-clean-360
Generate embedding vectors python speaker_encoder/compute_embeddings.py --use_cuda true /model/path/best_model.pth model/config/path/config.json dataset/path/ output_path . This code parses all .wav files at the given dataset path and generates the same folder structure under the output path with the generated embedding files.
Watch training on Tensorboard as in TTS