revised env setup, random seed effective
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README.md
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README.md
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@ -19,7 +19,7 @@ To clone or edit an unseen voice, VoiceCraft needs only a few seconds of referen
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- [x] Model weights (both 330M and 830M, the former seems to be just as good)
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- [ ] Write colab notebooks for better hands-on experience
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- [ ] HuggingFace Spaces demo
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- [ ] Better guidance on training
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- [ ] Better guidance on training/finetuning
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## How to run TTS inference
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There are two ways:
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@ -28,6 +28,8 @@ There are two ways:
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When you are inside the docker image or you have installed all dependencies, Checkout [`inference_tts.ipynb`](./inference_tts.ipynb).
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If you want to do model development such as training/finetuning, I recommend following [envrionment setup](#environment-setup) and [training](#training).
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## QuickStart
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:star: To try out TTS inference with VoiceCraft, the best way is using docker. Thank [@ubergarm](https://github.com/ubergarm) and [@jayc88](https://github.com/jay-c88) for making this happen.
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@ -66,13 +68,13 @@ echo GOOD LUCK
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conda create -n voicecraft python=3.9.16
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conda activate voicecraft
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pip install torch==2.0.1 # this assumes your system is compatible with CUDA 11.7, otherwise checkout https://pytorch.org/get-started/previous-versions/#v201
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apt-get install ffmpeg # if you don't already have ffmpeg installed
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pip install -e git+https://github.com/facebookresearch/audiocraft.git@c5157b5bf14bf83449c17ea1eeb66c19fb4bc7f0#egg=audiocraft
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pip install xformers==0.0.22
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pip install torchaudio==2.0.2 torch==2.0.1 # this assumes your system is compatible with CUDA 11.7, otherwise checkout https://pytorch.org/get-started/previous-versions/#v201
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apt-get install ffmpeg # if you don't already have ffmpeg installed
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apt-get install espeak-ng # backend for the phonemizer installed below
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pip install tensorboard==2.16.2
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pip install phonemizer==3.2.1
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pip install torchaudio==2.0.2
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pip install datasets==2.16.0
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pip install torchmetrics==0.11.1
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# install MFA for getting forced-alignment, this could take a few minutes
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@ -80,7 +82,7 @@ conda install -c conda-forge montreal-forced-aligner=2.2.17 openfst=1.8.2 kaldi=
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# conda install pocl # above gives an warning for installing pocl, not sure if really need this
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# to run ipynb
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conda install -n voicecraft ipykernel --update-deps --force-reinstall
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conda install -n voicecraft ipykernel --no-deps --force-reinstall
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```
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If you have encountered version issues when running things, checkout [environment.yml](./environment.yml) for exact matching.
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@ -129,7 +131,7 @@ bash e830M.sh
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## License
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The codebase is under CC BY-NC-SA 4.0 ([LICENSE-CODE](./LICENSE-CODE)), and the model weights are under Coqui Public Model License 1.0.0 ([LICENSE-MODEL](./LICENSE-MODEL)). Note that we use some of the code from other repository that are under different licenses: `./models/codebooks_patterns.py` is under MIT license; `./models/modules`, `./steps/optim.py`, `data/tokenizer.py` are under Apache License, Version 2.0; the phonemizer we used is under GNU 3.0 License. For drop-in replacement of the phonemizer (i.e. text to IPA phoneme mapping), try [g2p](https://github.com/roedoejet/g2p) (MIT License) or [OpenPhonemizer](https://github.com/NeuralVox/OpenPhonemizer) (BSD-3-Clause Clear), although these are not tested.
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The codebase is under CC BY-NC-SA 4.0 ([LICENSE-CODE](./LICENSE-CODE)), and the model weights are under Coqui Public Model License 1.0.0 ([LICENSE-MODEL](./LICENSE-MODEL)). Note that we use some of the code from other repository that are under different licenses: `./models/codebooks_patterns.py` is under MIT license; `./models/modules`, `./steps/optim.py`, `data/tokenizer.py` are under Apache License, Version 2.0; the phonemizer we used is under GNU 3.0 License.
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<!-- How to use g2p to convert english text into IPA phoneme sequence
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first install it with `pip install g2p`
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@ -30,13 +30,15 @@
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"# import libs\n",
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"import torch\n",
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"import torchaudio\n",
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"import numpy as np\n",
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"import random\n",
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"\n",
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"from data.tokenizer import (\n",
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" AudioTokenizer,\n",
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" TextTokenizer,\n",
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")\n",
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"\n",
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"from models import voicecraft\n"
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"from models import voicecraft"
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]
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},
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{
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@ -72,6 +74,15 @@
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"silence_tokens = [1388,1898,131] # if there are long silence in the generated audio, reduce the stop_repetition to 3, 2 or even 1\n",
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"stop_repetition = -1 # -1 means do not adjust prob of silence tokens. if there are long silence or unnaturally strecthed words, increase sample_batch_size to 2, 3 or even 4\n",
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"# what this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest\n",
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"def seed_everything(seed):\n",
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" os.environ['PYTHONHASHSEED'] = str(seed)\n",
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" random.seed(seed)\n",
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" np.random.seed(seed)\n",
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" torch.manual_seed(seed)\n",
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" torch.cuda.manual_seed(seed)\n",
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" torch.backends.cudnn.benchmark = False\n",
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" torch.backends.cudnn.deterministic = True\n",
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"seed_everything(seed)\n",
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
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"\n",
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"# point to the original file or record the file\n",
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@ -122,11 +122,13 @@
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"\n",
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"import torch\n",
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"import torchaudio\n",
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"import numpy as np\n",
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"import random\n",
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"\n",
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"from data.tokenizer import (\n",
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" AudioTokenizer,\n",
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" TextTokenizer,\n",
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")"
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")\n"
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]
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},
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{
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@ -241,6 +243,16 @@
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"sample_batch_size = 4 # NOTE: if the if there are long silence or unnaturally strecthed words, increase sample_batch_size to 5 or higher. What this will do to the model is that the model will run sample_batch_size examples of the same audio, and pick the one that's the shortest. So if the speech rate of the generated is too fast change it to a smaller number.\n",
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"seed = 1 # change seed if you are still unhappy with the result\n",
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"\n",
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"def seed_everything(seed):\n",
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" os.environ['PYTHONHASHSEED'] = str(seed)\n",
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" random.seed(seed)\n",
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" np.random.seed(seed)\n",
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" torch.manual_seed(seed)\n",
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" torch.cuda.manual_seed(seed)\n",
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" torch.backends.cudnn.benchmark = False\n",
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" torch.backends.cudnn.deterministic = True\n",
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"seed_everything(seed)\n",
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"\n",
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"decode_config = {'top_k': top_k, 'top_p': top_p, 'temperature': temperature, 'stop_repetition': stop_repetition, 'kvcache': kvcache, \"codec_audio_sr\": codec_audio_sr, \"codec_sr\": codec_sr, \"silence_tokens\": silence_tokens, \"sample_batch_size\": sample_batch_size}\n",
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"from inference_tts_scale import inference_one_sample\n",
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"concated_audio, gen_audio = inference_one_sample(model, ckpt[\"config\"], phn2num, text_tokenizer, audio_tokenizer, audio_fn, target_transcript, device, decode_config, prompt_end_frame)\n",
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@ -280,7 +292,7 @@
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"kernelspec": {
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"display_name": "voicecraft",
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"language": "python",
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"name": "voicecraft"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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@ -292,7 +304,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.19"
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"version": "3.9.18"
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}
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},
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"nbformat": 4,
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