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First draft for script/Dockerimage
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Thorsten Mueller
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README.md
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README.md
@ -78,6 +78,14 @@ To get an impression what my voice sounds to decide if it fits to your project i
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> If you trained a model on "thorsten" dataset please file an issue with some information on it. Sharing a trained model is highly appreciated.
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## Trained models (with at least acceptable) quality
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Inside the "models" (sub)folders are configs and Dockerfiles for a specific training from scratch.
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> Thanks to @erogol and @repodiac for brining in idea/code for script/container files.
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| Folder | date of training | branch | description |
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|--------|------------------|-------------|---|---|
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|thorsten-taco2-v0.0.1|august 2020| dev | pure taco2 training without vocoder|
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|thorsten-taco2-v0.0.1|to do| to do | to do |
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# Feel free to file an issue if you ...
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* have improvements on dataset
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@ -85,7 +93,6 @@ To get an impression what my voice sounds to decide if it fits to your project i
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* want to share your trained "thorsten" model
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* get to know about any abuse usage of my voice
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# Special thanks
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I want to thank all open source communities for providing great projects.
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@ -111,6 +118,7 @@ Thank you Dominik (@domcross / https://github.com/domcross/)
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* https://github.com/MycroftAI/mimic-recording-studio
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* https://voice.mozilla.org/
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* https://github.com/mozilla/TTS
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(https://github.com/repodiac/tit-for-tat/tree/master/thorsten-TTS)
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* https://raw.githubusercontent.com/mozilla/voice-web/master/server/data/de/sentence-collector.txt
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We'll hear us in future :-)
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68
models/thorsten-taco2-v0.0.1/Dockerfile
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models/thorsten-taco2-v0.0.1/Dockerfile
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# Adapted from @thorstenMueller's training script (https://github.com/thorstenMueller/TTS_recipes)
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# Docker file by @repodiac (https://github.com/repodiac/tit-for-tat/thorsten-tts)
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# *** Use without warranty and at your own risk! ***
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# Installation folder **inside** Docker container
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# (NOTE: if it is changed to another folder, you have to manually change it to the same folder in the last line, ENTRYPOINT ...)
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ARG BASEDIR=/tmp/tts
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FROM pytorch/pytorch:1.6.0-cuda10.1-cudnn7-devel as ttts-base
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ARG BASEDIR
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WORKDIR $BASEDIR
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# Install system libraries etc.
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FROM ttts-base as ttts1
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ARG BASEDIR
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WORKDIR $BASEDIR
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RUN apt-get update
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RUN apt-get install -y --no-install-recommends build-essential gcc espeak-ng espeak-ng-data git git-extras
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RUN pip install pip --upgrade
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RUN pip install gdown
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# Clone deep-learning-german-tts repo and copy config and test sentences
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RUN git clone --single-branch --branch dev https://github.com/thorstenMueller/deep-learning-german-tts.git
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RUN cp $BASEDIR/deep-learning-german-tts/models/thorsten-taco2-v0.0.1/de-test-sentences.txt $BASEDIR
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RUN cp $BASEDIR/deep-learning-german-tts/models/thorsten-taco2-v0.0.1/config.json $BASEDIR
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# Download and extract "thorsten-TTS" dataset
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FROM ttts1 as ttts2
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ARG BASEDIR
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WORKDIR $BASEDIR
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RUN cd $BASEDIR
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RUN gdown https://drive.google.com/uc?id=1yKJM1LAOQpRVojKunD9r8WN_p5KzBxjc -O thorsten-dataset.tgz
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RUN tar -xvzf thorsten-dataset.tgz
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# Prepare shuffled training and validate data (90% train, 10% val)
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RUN shuf LJSpeech-1.1/metadata.csv > LJSpeech-1.1/metadata_shuf.csv
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RUN head -n 20400 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_train.csv
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RUN tail -n 2268 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_val.csv
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# Install Mozilla TTS repo and dependencies
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FROM ttts2 as ttts3
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ARG BASEDIR
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WORKDIR $BASEDIR
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RUN git clone --single-branch --branch dev https://github.com/mozilla/TTS $BASEDIR/TTS
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WORKDIR $BASEDIR/TTS
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RUN python setup.py develop
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# Add german phoneme cleaner library by @repodiac
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FROM ttts3 as ttts4
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ARG BASEDIR
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RUN git clone https://github.com/repodiac/german_transliterate $BASEDIR/german_transliterate
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WORKDIR $BASEDIR/german_transliterate
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RUN pip install -e .
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WORKDIR $BASEDIR/TTS/mozilla_voice_tts/tts/utils/text
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RUN sed '/import re/a from german_transliterate.core import GermanTransliterate' cleaners.py >> cleaners-new.py
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RUN mv cleaners-new.py cleaners.py
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RUN echo "\ndef german_phoneme_cleaners(text):" >> cleaners.py
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RUN echo "\treturn GermanTransliterate(replace={';': ',', ':': ' '}, sep_abbreviation=' -- ').transliterate(text)" >> cleaners.py
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# Run training
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WORKDIR $BASEDIR/TTS/mozilla_voice_tts/bin/
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ENTRYPOINT CUDA_VISIBLE_DEVICES="0" python train_tts.py --config_path $BASEDIR/config.json
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0
models/thorsten-taco2-v0.0.1/README.md
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0
models/thorsten-taco2-v0.0.1/README.md
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models/thorsten-taco2-v0.0.1/config.json
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models/thorsten-taco2-v0.0.1/config.json
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{
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"github_branch":"* master",
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"restore_path":"",
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"model": "Tacotron2", // one of the model in models/
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"run_name": "thorsten-v1.0.0",
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"run_description": "thorsten-de v0.0.1",
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// AUDIO PARAMETERS
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"audio":{
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// New "dev" branch params
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"fft_size": 1024,
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"spec_gain": 1.0,
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// Audio processing parameters
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"num_mels": 80, // size of the mel spec frame.
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"num_freq": 1025, // number of stft frequency levels. Size of the linear spectogram frame.
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"sample_rate": 22050, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
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"win_length": 1024, // stft window length in ms.
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"hop_length": 256, // stft window hop-lengh in ms.
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"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
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"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
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"preemphasis": 0.0, //0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
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"min_level_db": -100, // normalization range
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"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
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"power": 1.5, // value to sharpen wav signals after GL algorithm.
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"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
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// Normalization parameters
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"signal_norm": true, // normalize the spec values in range [0, 1]
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"symmetric_norm": true, // move normalization to range [-1, 1]
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"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"clip_norm": true, // clip normalized values into the range.
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"mel_fmin": 75.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 7750.0, // maximum freq level for mel-spec. Tune for dataset!!
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"do_trim_silence": true, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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"trim_db": 60 // threshold for timming silence. Set this according to your dataset.
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},
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// VOCABULARY PARAMETERS
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// if custom character set is not defined,
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// default set in symbols.py is used
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"characters":{
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"pad": "_",
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"eos": "~",
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"bos": "^",
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"characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz!'(),-.:;? äöüÄÖÜß",
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"punctuations":"!'(),-.:;? ",
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"phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
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},
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// DISTRIBUTED TRAINING
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"distributed":{
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"backend": "nccl",
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"url": "tcp:\/\/localhost:54321"
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},
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"reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
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// TRAINING
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"batch_size": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
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"eval_batch_size":16,
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"r": 7, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
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"gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [100000, 2, 32], [200000, 1, 32]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
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"loss_masking": true, // enable / disable loss masking against the sequence padding.
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"ga_alpha": 10.0, // weight for guided attention loss. If > 0, guided attention is enabled.
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"apex_amp_level": null,
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// VALIDATION
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"run_eval": true,
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"test_delay_epochs": 10, //Until attention is aligned, testing only wastes computation time.
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"test_sentences_file": "/tmp/tts/de-test-sentences.txt", // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
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// OPTIMIZER
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"noam_schedule": false, // use noam warmup and lr schedule.
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"grad_clip": 1.0, // upper limit for gradients for clipping.
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"epochs": 1000, // total number of epochs to train.
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"lr": 0.0005, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"wd": 0.000001, // Weight decay weight.
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"seq_len_norm": false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
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// TACOTRON PRENET
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"memory_size": -1, // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
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"prenet_type": "original", // "original" or "bn".
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"prenet_dropout": true, // enable/disable dropout at prenet.
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// ATTENTION
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"attention_type": "original", // 'original' or 'graves'
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"attention_heads": 4, // number of attention heads (only for 'graves')
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"attention_norm": "softmax", // softmax or sigmoid. Suggested to use softmax for Tacotron2 and sigmoid for Tacotron.
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"windowing": false, // Enables attention windowing. Used only in eval mode.
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"use_forward_attn": true, // if it uses forward attention. In general, it aligns faster.
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"forward_attn_mask": false, // Additional masking forcing monotonicity only in eval mode.
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"transition_agent": false, // enable/disable transition agent of forward attention.
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"location_attn": true, // enable_disable location sensitive attention. It is enabled for TACOTRON by default.
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"bidirectional_decoder": false, // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
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"double_decoder_consistency": true,
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"ddc_r": 7,
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// STOPNET
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"stopnet": true, // Train stopnet predicting the end of synthesis.
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"separate_stopnet": true, // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
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// TENSORBOARD and LOGGING
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"print_step": 25, // Number of steps to log traning on console.
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"tb_plot_step": 100,
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"print_eval": false, // If True, it prints intermediate loss values in evalulation.
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"save_step": 5000, // Number of training steps expected to save traninpg stats and checkpoints.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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// DATA LOADING
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//"text_cleaner": "phoneme_cleaners",
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"text_cleaner": "german_phoneme_cleaners",
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"enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
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"num_loader_workers": 8, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"num_val_loader_workers": 4, // number of evaluation data loader processes.
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"batch_group_size": 0, //Number of batches to shuffle after bucketing.
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"min_seq_len": 3, // DATASET-RELATED: minimum text length to use in training
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"max_seq_len": 180, // DATASET-RELATED: maximum text length
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// PATHS
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"output_path": "/tmp/tts/models/thorsten/",
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// PHONEMES
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"phoneme_cache_path": "mozilla_de_phonemes", // phoneme computation is slow, therefore, it caches results in the given folder.
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"use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronounciation.
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"phoneme_language": "de", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
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// MULTI-SPEAKER and GST
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"use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
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"style_wav_for_test": null, // path to style wav file to be used in TacotronGST inference.
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"use_gst": false, // TACOTRON ONLY: use global style tokens
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// DATASETS
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"datasets": // List of datasets. They all merged and they get different speaker_ids.
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[
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{
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"name": "ljspeech",
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"path": "/tmp/tts/LJSpeech-1.1",
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"meta_file_train": "metadata_train.csv",
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"meta_file_val": "metadata_val.csv"
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}
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]
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}
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7
models/thorsten-taco2-v0.0.1/de-test-sentences.txt
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7
models/thorsten-taco2-v0.0.1/de-test-sentences.txt
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Die aktuelle Außentemperatur beträgt sieben Grad Celsius und die Regenwahrscheinlichkeit liegt bei zwölf Prozent.
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Die aktuelle Außentemperatur beträgt 7°C und die Regenwahrscheinlichkeit liegt bei 12%.
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Frankfurt am Main wird auch Mainhattan genannt.
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Ich bedanke mich bei euch für euren Support und eure Gedult bei der Erzeugung einer künstlichen Stimme.
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Hallo, wie geht es Dir?
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Was ist los!
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Die wachsende Furcht vor den Folgen des grassierenden Coronavirus für die Weltwirtschaft hat den Dax am Dienstag auf das tiefste Niveau seit Oktober vergangenen Jahres gedrückt.
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