mirror of https://github.com/coqui-ai/TTS.git
6fdb88f8e2
* add configs * Update config file * Add model configs * Add model layers * Add layer files * Add layer modules * change config names * Add emotion manager * fIX missing ap bug * Fix missing ap bug * Add base TTS e2e class * Fix wrong variable name in load_tts_samples * Add training script * Remove range predictor and gaussian upsampling * Add helper function * Add vctk recipe * Add conformer docs * Fix linting in conformer.py * Add Docs * remove duplicate import * refactor args * Fix bugs * Removew emotion embedding * remove unused arg * Remove emotion embedding arg * Remove emotion embedding arg * fix style issues * Fix bugs * Fix bugs * Add unittests * make style * fix formatter bug * fix test * Add pyworld compute pitch func * Update requirments.txt * Fix dataset Bug * Chnge layer norm to instance norm * Add missing import * Remove emotions.py * remove ssim loss * Add init layers func to aligner * refactor model layers * remove audio_config arg * Rename loss func * Rename to delightful-tts * Rename loss func * Remove unused modules * refactor imports * replace audio config with audio processor * Add change sample rate option * remove broken resample func * update recipe * fix style, add config docs * fix tests and multispeaker embd dim * remove pyworld * Make style and fix inference * Split tts tests * Fixup * Fixup * Fixup * Add argument names * Set "random" speaker in the model Tortoise/Bark * Use a diff f0_cache path for delightfull tts * Fix delightful speaker handling * Fix lint * Make style --------- Co-authored-by: loganhart420 <loganartpersonal@gmail.com> Co-authored-by: Eren Gölge <erogol@hotmail.com> |
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| .. | ||
| blizzard2013 | ||
| kokoro/tacotron2-DDC | ||
| ljspeech | ||
| multilingual/vits_tts | ||
| thorsten_DE | ||
| vctk | ||
| README.md | ||
README.md
🐸💬 TTS Training Recipes
TTS recipes intended to host scripts running all the necessary steps to train a TTS model on a particular dataset.
For each dataset, you need to download the dataset once. Then you run the training for the model you want.
Run each script from the root TTS folder as follows.
$ sh ./recipes/<dataset>/download_<dataset>.sh
$ python recipes/<dataset>/<model_name>/train.py
For some datasets you might need to resample the audio files. For example, VCTK dataset can be resampled to 22050Hz as follows.
python TTS/bin/resample.py --input_dir recipes/vctk/VCTK/wav48_silence_trimmed --output_sr 22050 --output_dir recipes/vctk/VCTK/wav48_silence_trimmed --n_jobs 8 --file_ext flac
If you train a new model using TTS, feel free to share your training to expand the list of recipes.
You can also open a new discussion and share your progress with the 🐸 community.