Training recipes for thorsten dataset (#1020)

* Fix style

* Fix isort

* Remove tensorboardX from requirements

Co-authored-by: logan hart <72301874+loganhart420@users.noreply.github.com>
Co-authored-by: Eren Gölge <egolge@coqui.ai>

.github/workflows/docker.yaml

@@ -62,4 +62,4 @@ jobs:
           platforms: linux/${{ matrix.arch }}
           push: ${{ github.event_name == 'push' }}
           build-args: "BASE=${{ matrix.base }}"
-          tags: ${{ steps.compute-tag.outputs.tags }}
+          tags: ${{ steps.compute-tag.outputs.tags }}

.gitignore

@@ -166,4 +166,4 @@ internal/*
 *_phoneme.npy
 wandb
 depot/*
-coqui_recipes/*
+coqui_recipes/*

Dockerfile

@@ -17,4 +17,4 @@ RUN ["/bin/bash", "-c", "pip install -r <(cat requirements.txt requirements.dev.
 COPY . /root
 RUN make install
 ENTRYPOINT ["tts"]
-CMD ["--help"]
+CMD ["--help"]

TTS/tts/datasets/formatters.py

@@ -141,6 +141,21 @@ def ljspeech_test(root_path, meta_file, **kwargs):  # pylint: disable=unused-arg
     return items
 
 
+def thorsten(root_path, meta_file, **kwargs):  # pylint: disable=unused-argument
+    """Normalizes the thorsten meta data file to TTS format
+    https://github.com/thorstenMueller/deep-learning-german-tts/"""
+    txt_file = os.path.join(root_path, meta_file)
+    items = []
+    speaker_name = "thorsten"
+    with open(txt_file, "r", encoding="utf-8") as ttf:
+        for line in ttf:
+            cols = line.split("|")
+            wav_file = os.path.join(root_path, "wavs", cols[0] + ".wav")
+            text = cols[1]
+            items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name})
+    return items
+
+
 def sam_accenture(root_path, meta_file, **kwargs):  # pylint: disable=unused-argument
     """Normalizes the sam-accenture meta data file to TTS format
     https://github.com/Sam-Accenture-Non-Binary-Voice/non-binary-voice-files"""

recipes/thorsten_DE/README.md

@@ -0,0 +1,15 @@
+# 🐸💬 TTS Thorsten Recipes
+
+For running the recipes you need the [Thorsten-Voice](https://github.com/thorstenMueller/Thorsten-Voice) dataset.
+
+You can download it manually from [the official website](https://www.thorsten-voice.de/) or use ```download_thorsten_de.sh``` alternatively running any of the **train_modelX.py**scripts will download the dataset if not already present.
+
+Then, go to your desired model folder and run the training.
+
+    Running Python files. (Choose the desired GPU ID for your run and set ```CUDA_VISIBLE_DEVICES```)
+    ```terminal
+    CUDA_VISIBLE_DEVICES="0" python train_modelX.py
+    ```
+
+💡 Note that these runs are just templates to help you start training your first model. They are not optimized for the best
+result. Double-check the configurations and feel free to share your experiments to find better parameters together 💪.

recipes/thorsten_DE/align_tts/train_aligntts.py

@@ -0,0 +1,84 @@
+import os
+
+from trainer import Trainer, TrainerArgs
+
+from TTS.tts.configs.align_tts_config import AlignTTSConfig
+from TTS.tts.configs.shared_configs import BaseDatasetConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models.align_tts import AlignTTS
+from TTS.tts.utils.text.tokenizer import TTSTokenizer
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.downloaders import download_thorsten_de
+
+output_path = os.path.dirname(os.path.abspath(__file__))
+
+# init configs
+dataset_config = BaseDatasetConfig(
+    name="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
+)
+
+# download dataset if not already present
+if not os.path.exists(dataset_config.path):
+    print("Downloading dataset")
+    download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0])
+
+config = AlignTTSConfig(
+    batch_size=32,
+    eval_batch_size=16,
+    num_loader_workers=4,
+    num_eval_loader_workers=4,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1000,
+    text_cleaner="phoneme_cleaners",
+    use_phonemes=False,
+    phoneme_language="de",
+    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
+    print_step=25,
+    print_eval=True,
+    mixed_precision=False,
+    test_sentences=[
+        "Es hat mich viel Zeit gekostet ein Stimme zu entwickeln, jetzt wo ich sie habe werde ich nicht mehr schweigen.",
+        "Sei eine Stimme, kein Echo.",
+        "Es tut mir Leid David. Das kann ich leider nicht machen.",
+        "Dieser Kuchen ist großartig. Er ist so lecker und feucht.",
+        "Vor dem 22. November 1963.",
+    ],
+    output_path=output_path,
+    datasets=[dataset_config],
+)
+
+# INITIALIZE THE AUDIO PROCESSOR
+# Audio processor is used for feature extraction and audio I/O.
+# It mainly serves to the dataloader and the training loggers.
+ap = AudioProcessor.init_from_config(config)
+
+# INITIALIZE THE TOKENIZER
+# Tokenizer is used to convert text to sequences of token IDs.
+# If characters are not defined in the config, default characters are passed to the config
+tokenizer, config = TTSTokenizer.init_from_config(config)
+
+# LOAD DATA SAMPLES
+# Each sample is a list of ```[text, audio_file_path, speaker_name]```
+# You can define your custom sample loader returning the list of samples.
+# Or define your custom formatter and pass it to the `load_tts_samples`.
+# Check `TTS.tts.datasets.load_tts_samples` for more details.
+train_samples, eval_samples = load_tts_samples(
+    dataset_config,
+    eval_split=True,
+    eval_split_max_size=config.eval_split_max_size,
+    eval_split_size=config.eval_split_size,
+)
+
+# init model
+model = AlignTTS(config, ap, tokenizer)
+
+# INITIALIZE THE TRAINER
+# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
+# distributed training, etc.
+trainer = Trainer(
+    TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
+)
+
+# AND... 3,2,1... 🚀
+trainer.fit()

recipes/thorsten_DE/download_thorsten_DE.sh

@@ -0,0 +1,21 @@
+# create venv
+python3 -m venv env
+source .env/bin/activate
+pip install pip --upgrade
+
+# download Thorsten_DE dataset
+pip install gdown
+gdown --id 1yKJM1LAOQpRVojKunD9r8WN_p5KzBxjc -O dataset.tgz
+tar -xzf dataset.tgz
+
+# create train-val splits
+shuf LJSpeech-1.1/metadata.csv > LJSpeech-1.1/metadata_shuf.csv
+head -n 20668 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_train.csv
+tail -n 2000 LJSpeech-1.1/metadata_shuf.csv > LJSpeech-1.1/metadata_val.csv
+
+# rename dataset and remove archive
+mv LJSpeech-1.1 thorsten-de
+rm dataset.tgz
+
+# destry venv
+rm -rf env

recipes/thorsten_DE/glow_tts/train_glowtts.py

@@ -0,0 +1,97 @@
+import os
+
+# Trainer: Where the ✨️ happens.
+# TrainingArgs: Defines the set of arguments of the Trainer.
+from trainer import Trainer, TrainerArgs
+
+# GlowTTSConfig: all model related values for training, validating and testing.
+from TTS.tts.configs.glow_tts_config import GlowTTSConfig
+
+# BaseDatasetConfig: defines name, formatter and path of the dataset.
+from TTS.tts.configs.shared_configs import BaseDatasetConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models.glow_tts import GlowTTS
+from TTS.tts.utils.text.tokenizer import TTSTokenizer
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.downloaders import download_thorsten_de
+
+# we use the same path as this script as our training folder.
+output_path = os.path.dirname(os.path.abspath(__file__))
+
+# DEFINE DATASET CONFIG
+# Set LJSpeech as our target dataset and define its path.
+# You can also use a simple Dict to define the dataset and pass it to your custom formatter.
+dataset_config = BaseDatasetConfig(
+    name="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
+)
+
+# download dataset if not already present
+if not os.path.exists(dataset_config.path):
+    print("Downloading dataset")
+    download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0])
+
+# INITIALIZE THE TRAINING CONFIGURATION
+# Configure the model. Every config class inherits the BaseTTSConfig.
+config = GlowTTSConfig(
+    batch_size=32,
+    eval_batch_size=16,
+    num_loader_workers=4,
+    num_eval_loader_workers=4,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1000,
+    text_cleaner="phoneme_cleaners",
+    use_phonemes=True,
+    phoneme_language="de",
+    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
+    print_step=25,
+    print_eval=False,
+    mixed_precision=True,
+    test_sentences=[
+        "Es hat mich viel Zeit gekostet ein Stimme zu entwickeln, jetzt wo ich sie habe werde ich nicht mehr schweigen.",
+        "Sei eine Stimme, kein Echo.",
+        "Es tut mir Leid David. Das kann ich leider nicht machen.",
+        "Dieser Kuchen ist großartig. Er ist so lecker und feucht.",
+        "Vor dem 22. November 1963.",
+    ],
+    output_path=output_path,
+    datasets=[dataset_config],
+)
+
+# INITIALIZE THE AUDIO PROCESSOR
+# Audio processor is used for feature extraction and audio I/O.
+# It mainly serves to the dataloader and the training loggers.
+ap = AudioProcessor.init_from_config(config)
+
+# INITIALIZE THE TOKENIZER
+# Tokenizer is used to convert text to sequences of token IDs.
+# If characters are not defined in the config, default characters are passed to the config
+tokenizer, config = TTSTokenizer.init_from_config(config)
+
+# LOAD DATA SAMPLES
+# Each sample is a list of ```[text, audio_file_path, speaker_name]```
+# You can define your custom sample loader returning the list of samples.
+# Or define your custom formatter and pass it to the `load_tts_samples`.
+# Check `TTS.tts.datasets.load_tts_samples` for more details.
+train_samples, eval_samples = load_tts_samples(
+    dataset_config,
+    eval_split=True,
+    eval_split_max_size=config.eval_split_max_size,
+    eval_split_size=config.eval_split_size,
+)
+
+# INITIALIZE THE MODEL
+# Models take a config object and a speaker manager as input
+# Config defines the details of the model like the number of layers, the size of the embedding, etc.
+# Speaker manager is used by multi-speaker models.
+model = GlowTTS(config, ap, tokenizer, speaker_manager=None)
+
+# INITIALIZE THE TRAINER
+# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
+# distributed training, etc.
+trainer = Trainer(
+    TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
+)
+
+# AND... 3,2,1... 🚀
+trainer.fit()

recipes/thorsten_DE/hifigan/train_hifigan.py

@@ -0,0 +1,53 @@
+import os
+
+from trainer import Trainer, TrainerArgs
+
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.downloaders import download_thorsten_de
+from TTS.vocoder.configs import HifiganConfig
+from TTS.vocoder.datasets.preprocess import load_wav_data
+from TTS.vocoder.models.gan import GAN
+
+output_path = os.path.dirname(os.path.abspath(__file__))
+
+config = HifiganConfig(
+    batch_size=32,
+    eval_batch_size=16,
+    num_loader_workers=4,
+    num_eval_loader_workers=4,
+    run_eval=True,
+    test_delay_epochs=5,
+    epochs=1000,
+    seq_len=8192,
+    pad_short=2000,
+    use_noise_augment=True,
+    eval_split_size=10,
+    print_step=25,
+    print_eval=False,
+    mixed_precision=False,
+    lr_gen=1e-4,
+    lr_disc=1e-4,
+    data_path=os.path.join(output_path, "../thorsten-de/wavs/"),
+    output_path=output_path,
+)
+
+# download dataset if not already present
+if not os.path.exists(config.data_path):
+    print("Downloading dataset")
+    download_path = os.path.abspath(os.path.join(os.path.abspath(config.data_path), "../../"))
+    download_thorsten_de(download_path)
+
+# init audio processor
+ap = AudioProcessor(**config.audio.to_dict())
+
+# load training samples
+eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
+
+# init model
+model = GAN(config, ap)
+
+# init the trainer and 🚀
+trainer = Trainer(
+    TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
+)
+trainer.fit()

recipes/thorsten_DE/multiband_melgan/train_multiband_melgan.py

@@ -0,0 +1,53 @@
+import os
+
+from trainer import Trainer, TrainerArgs
+
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.downloaders import download_thorsten_de
+from TTS.vocoder.configs import MultibandMelganConfig
+from TTS.vocoder.datasets.preprocess import load_wav_data
+from TTS.vocoder.models.gan import GAN
+
+output_path = os.path.dirname(os.path.abspath(__file__))
+
+config = MultibandMelganConfig(
+    batch_size=32,
+    eval_batch_size=16,
+    num_loader_workers=4,
+    num_eval_loader_workers=4,
+    run_eval=True,
+    test_delay_epochs=5,
+    epochs=1000,
+    seq_len=8192,
+    pad_short=2000,
+    use_noise_augment=True,
+    eval_split_size=10,
+    print_step=25,
+    print_eval=False,
+    mixed_precision=False,
+    lr_gen=1e-4,
+    lr_disc=1e-4,
+    data_path=os.path.join(output_path, "../thorsten-de/wavs/"),
+    output_path=output_path,
+)
+
+# download dataset if not already present
+if not os.path.exists(config.data_path):
+    print("Downloading dataset")
+    download_path = os.path.abspath(os.path.join(os.path.abspath(config.data_path), "../../"))
+    download_thorsten_de(download_path)
+
+# init audio processor
+ap = AudioProcessor(**config.audio.to_dict())
+
+# load training samples
+eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
+
+# init model
+model = GAN(config, ap)
+
+# init the trainer and 🚀
+trainer = Trainer(
+    TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
+)
+trainer.fit()