PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

Last update: Jan 08, 2023

Overview

Deepvoice3_pytorch

PyTorch implementation of convolutional networks-based text-to-speech synthesis models:

arXiv:1710.07654: Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning.
arXiv:1710.08969: Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention.

Audio samples are available at https://r9y9.github.io/deepvoice3_pytorch/.

Folks

https://github.com/hash2430/dv3_world: DeepVoice3 with WORLD vocoder support. #166

Online TTS demo

Notebooks supposed to be executed on https://colab.research.google.com are available:

Highlights

Convolutional sequence-to-sequence model with attention for text-to-speech synthesis
Multi-speaker and single speaker versions of DeepVoice3
Audio samples and pre-trained models
Preprocessor for LJSpeech (en), JSUT (jp) and VCTK datasets, as well as carpedm20/multi-speaker-tacotron-tensorflow compatible custom dataset (in JSON format)
Language-dependent frontend text processor for English and Japanese

Samples

Pretrained models

NOTE: pretrained models are not compatible to master. To be updated soon.

URL	Model	Data	Hyper paramters	Git commit	Steps
link	DeepVoice3	LJSpeech	link	abf0a21	640k
link	Nyanko	LJSpeech	`builder=nyanko,preset=nyanko_ljspeech`	ba59dc7	585k
link	Multi-speaker DeepVoice3	VCTK	`builder=deepvoice3_multispeaker,preset=deepvoice3_vctk`	0421749	300k + 300k

To use pre-trained models, it's highly recommended that you are on the specific git commit noted above. i.e.,

git checkout ${commit_hash}

Then follow the "Synthesize from a checkpoint" section in the README of the specific git commit. Please notice that the latest development version of the repository may not work.

You could try for example:

# pretrained model (20180505_deepvoice3_checkpoint_step000640000.pth)
# hparams (20180505_deepvoice3_ljspeech.json)
git checkout 4357976
python synthesis.py --preset=20180505_deepvoice3_ljspeech.json \
  20180505_deepvoice3_checkpoint_step000640000.pth \
  sentences.txt \
  output_dir

Notes on hyper parameters

Default hyper parameters, used during preprocessing/training/synthesis stages, are turned for English TTS using LJSpeech dataset. You will have to change some of parameters if you want to try other datasets. See hparams.py for details.
builder specifies which model you want to use. deepvoice3, deepvoice3_multispeaker [1] and nyanko [2] are surpprted.
Hyper parameters described in DeepVoice3 paper for single speaker didn't work for LJSpeech dataset, so I changed a few things. Add dilated convolution, more channels, more layers and add guided attention loss, etc. See code for details. The changes are also applied for multi-speaker model.
Multiple attention layers are hard to learn. Empirically, one or two (first and last) attention layers seems enough.
With guided attention (see https://arxiv.org/abs/1710.08969), alignments get monotonic more quickly and reliably if we use multiple attention layers. With guided attention, I can confirm five attention layers get monotonic, though I cannot get speech quality improvements.
Binary divergence (described in https://arxiv.org/abs/1710.08969) seems stabilizes training particularly for deep (> 10 layers) networks.
Adam with step lr decay works. However, for deeper networks, I find Adam + noam's lr scheduler is more stable.

Requirements

Python >= 3.5
CUDA >= 8.0
PyTorch >= v1.0.0
nnmnkwii >= v0.0.11
MeCab (Japanese only)

Installation

Please install packages listed above first, and then

git clone https://github.com/r9y9/deepvoice3_pytorch && cd deepvoice3_pytorch
pip install -e ".[bin]"

Getting started

Preset parameters

There are many hyper parameters to be turned depends on what model and data you are working on. For typical datasets and models, parameters that known to work good (preset) are provided in the repository. See presets directory for details. Notice that

preprocess.py
train.py
synthesis.py

accepts --preset=<json> optional parameter, which specifies where to load preset parameters. If you are going to use preset parameters, then you must use same --preset=<json> throughout preprocessing, training and evaluation. e.g.,

python preprocess.py --preset=presets/deepvoice3_ljspeech.json ljspeech ~/data/LJSpeech-1.0
python train.py --preset=presets/deepvoice3_ljspeech.json --data-root=./data/ljspeech

instead of

python preprocess.py ljspeech ~/data/LJSpeech-1.0
# warning! this may use different hyper parameters used at preprocessing stage
python train.py --preset=presets/deepvoice3_ljspeech.json --data-root=./data/ljspeech

0. Download dataset

LJSpeech (en): https://keithito.com/LJ-Speech-Dataset/
VCTK (en): http://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html
JSUT (jp): https://sites.google.com/site/shinnosuketakamichi/publication/jsut
NIKL (ko) (Need korean cellphone number to access it): http://www.korean.go.kr/front/board/boardStandardView.do?board_id=4&mn_id=17&b_seq=464

1. Preprocessing

Usage:

python preprocess.py ${dataset_name} ${dataset_path} ${out_dir} --preset=<json>

Supported ${dataset_name}s are:

ljspeech (en, single speaker)
vctk (en, multi-speaker)
jsut (jp, single speaker)
nikl_m (ko, multi-speaker)
nikl_s (ko, single speaker)

Assuming you use preset parameters known to work good for LJSpeech dataset / DeepVoice3 and have data in ~/data/LJSpeech-1.0, then you can preprocess data by:

python preprocess.py --preset=presets/deepvoice3_ljspeech.json ljspeech ~/data/LJSpeech-1.0/ ./data/ljspeech

When this is done, you will see extracted features (mel-spectrograms and linear spectrograms) in ./data/ljspeech.

1-1. Building custom dataset. (using json_meta)

Building your own dataset, with metadata in JSON format (compatible with carpedm20/multi-speaker-tacotron-tensorflow) is currently supported. Usage:

python preprocess.py json_meta ${list-of-JSON-metadata-paths} ${out_dir} --preset=<json>

You may need to modify pre-existing preset JSON file, especially n_speakers. For english multispeaker, start with presets/deepvoice3_vctk.json.

Assuming you have dataset A (Speaker A) and dataset B (Speaker B), each described in the JSON metadata file ./datasets/datasetA/alignment.json and ./datasets/datasetB/alignment.json, then you can preprocess data by:

python preprocess.py json_meta "./datasets/datasetA/alignment.json,./datasets/datasetB/alignment.json" "./datasets/processed_A+B" --preset=(path to preset json file)

1-2. Preprocessing custom english datasets with long silence. (Based on vctk_preprocess)

Some dataset, especially automatically generated dataset may include long silence and undesirable leading/trailing noises, undermining the char-level seq2seq model. (e.g. VCTK, although this is covered in vctk_preprocess)

To deal with the problem, gentle_web_align.py will

Prepare phoneme alignments for all utterances
Cut silences during preprocessing

gentle_web_align.py uses Gentle, a kaldi based speech-text alignment tool. This accesses web-served Gentle application, aligns given sound segments with transcripts and converts the result to HTK-style label files, to be processed in preprocess.py. Gentle can be run in Linux/Mac/Windows(via Docker).

Preliminary results show that while HTK/festival/merlin-based method in vctk_preprocess/prepare_vctk_labels.py works better on VCTK, Gentle is more stable with audio clips with ambient noise. (e.g. movie excerpts)

Usage: (Assuming Gentle is running at localhost:8567 (Default when not specified))

When sound file and transcript files are saved in separate folders. (e.g. sound files are at datasetA/wavs and transcripts are at datasetA/txts)

python gentle_web_align.py -w "datasetA/wavs/*.wav" -t "datasetA/txts/*.txt" --server_addr=localhost --port=8567

When sound file and transcript files are saved in nested structure. (e.g. datasetB/speakerN/blahblah.wav and datasetB/speakerN/blahblah.txt)

python gentle_web_align.py --nested-directories="datasetB" --server_addr=localhost --port=8567

Once you have phoneme alignment for each utterance, you can extract features by running preprocess.py

2. Training

Usage:

python train.py --data-root=${data-root} --preset=<json> --hparams="parameters you may want to override"

Suppose you build a DeepVoice3-style model using LJSpeech dataset, then you can train your model by:

python train.py --preset=presets/deepvoice3_ljspeech.json --data-root=./data/ljspeech/

Model checkpoints (.pth) and alignments (.png) are saved in ./checkpoints directory per 10000 steps by default.

NIKL

Pleae check this in advance and follow the commands below.

python preprocess.py nikl_s ${your_nikl_root_path} data/nikl_s --preset=presets/deepvoice3_nikls.json

python train.py --data-root=./data/nikl_s --checkpoint-dir checkpoint_nikl_s --preset=presets/deepvoice3_nikls.json

4. Monitor with Tensorboard

Logs are dumped in ./log directory by default. You can monitor logs by tensorboard:

tensorboard --logdir=log

5. Synthesize from a checkpoint

Given a list of text, synthesis.py synthesize audio signals from trained model. Usage is:

python synthesis.py ${checkpoint_path} ${text_list.txt} ${output_dir} --preset=<json>

Example test_list.txt:

Generative adversarial network or variational auto-encoder.
Once upon a time there was a dear little girl who was loved by every one who looked at her, but most of all by her grandmother, and there was nothing that she would not have given to the child.
A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module.

Advanced usage

Multi-speaker model

VCTK and NIKL are supported dataset for building a multi-speaker model.

VCTK

Since some audio samples in VCTK have long silences that affect performance, it's recommended to do phoneme alignment and remove silences according to vctk_preprocess.

Once you have phoneme alignment for each utterance, you can extract features by:

python preprocess.py vctk ${your_vctk_root_path} ./data/vctk

Now that you have data prepared, then you can train a multi-speaker version of DeepVoice3 by:

python train.py --data-root=./data/vctk --checkpoint-dir=checkpoints_vctk \
   --preset=presets/deepvoice3_vctk.json \
   --log-event-path=log/deepvoice3_multispeaker_vctk_preset

If you want to reuse learned embedding from other dataset, then you can do this instead by:

python train.py --data-root=./data/vctk --checkpoint-dir=checkpoints_vctk \
   --preset=presets/deepvoice3_vctk.json \
   --log-event-path=log/deepvoice3_multispeaker_vctk_preset \
   --load-embedding=20171213_deepvoice3_checkpoint_step000210000.pth

This may improve training speed a bit.

NIKL

You will be able to obtain cleaned-up audio samples in ../nikl_preprocoess. Details are found in here.

Once NIKL corpus is ready to use from the preprocessing, you can extract features by:

python preprocess.py nikl_m ${your_nikl_root_path} data/nikl_m

Now that you have data prepared, then you can train a multi-speaker version of DeepVoice3 by:

python train.py --data-root=./data/nikl_m  --checkpoint-dir checkpoint_nikl_m \
   --preset=presets/deepvoice3_niklm.json

Speaker adaptation

If you have very limited data, then you can consider to try fine-turn pre-trained model. For example, using pre-trained model on LJSpeech, you can adapt it to data from VCTK speaker p225 (30 mins) by the following command:

python train.py --data-root=./data/vctk --checkpoint-dir=checkpoints_vctk_adaptation \
    --preset=presets/deepvoice3_ljspeech.json \
    --log-event-path=log/deepvoice3_vctk_adaptation \
    --restore-parts="20171213_deepvoice3_checkpoint_step000210000.pth"
    --speaker-id=0

From my experience, it can get reasonable speech quality very quickly rather than training the model from scratch.

There are two important options used above:

--restore-parts=<N>: It specifies where to load model parameters. The differences from the option --checkpoint=<N> are 1) --restore-parts=<N> ignores all invalid parameters, while --checkpoint=<N> doesn't. 2) --restore-parts=<N> tell trainer to start from 0-step, while --checkpoint=<N> tell trainer to continue from last step. --checkpoint=<N> should be ok if you are using exactly same model and continue to train, but it would be useful if you want to customize your model architecture and take advantages of pre-trained model.
--speaker-id=<N>: It specifies what speaker of data is used for training. This should only be specified if you are using multi-speaker dataset. As for VCTK, speaker id is automatically assigned incrementally (0, 1, ..., 107) according to the speaker_info.txt in the dataset.

If you are training multi-speaker model, speaker adaptation will only work when n_speakers is identical.

Trouble shooting

#5 RuntimeError: main thread is not in main loop

This may happen depending on backends you have for matplotlib. Try changing backend for matplotlib and see if it works as follows:

MPLBACKEND=Qt5Agg python train.py ${args...}

In #78, engiecat reported that changing the backend of matplotlib from Tkinter(TkAgg) to PyQt5(Qt5Agg) fixed the problem.

Sponsers

https://github.com/echelon

Acknowledgements

Part of code was adapted from the following projects:

Banner and logo created by @jraulhernandezi (#76)

Comments

TODOs, status and progress
Single speaker model

Data: https://keithito.com/LJ-Speech-Dataset/

[x] Convolution layers

[x] Multi-hop attention layers

[x] Attention mask for input zero padding

[x] Alignments are learned almost monotonically

[x] Incremental inference (greedy decoding)

[x] Force monotonic attention

[ ] Done flag prediction

[x] Get reasonable sound quality as Tacotron (https://github.com/r9y9/tacotron_pytorch)

[x] Audio samples (en)

~~Audio samples (jp)~~

[x] Pre-trained models

Multi-speaker model

Data: VCTK

[x] Preprocessor for VCTK

[x] Speaker embedding

[x] Get reasonable sound quality

[x] Audio samples

[x] Pre-trained model

Misc

[x] Char and phoneme mixed inputs

[x] Japanese text-processing frontend

[x] Try Japanese TTS using https://sites.google.com/site/shinnosuketakamichi/publication/jsut

[x] Implement dilated convolution

[x] preprocessor for jsut

[x] Integrate https://github.com/lanpa/tensorboard-pytorch and log images and audio samples

[x] Add instructions how to train models (en/jp)

[x] Rewrite audio module for better spectrogram representation. Replace griffin lim with https://github.com/Jonathan-LeRoux/lws.

[x] Create github pages with speech samples

From https://arxiv.org/abs/1710.08969

[x] Guided attention

[x] Downsample mel-spectrogram / upsample converter

[x] Binary divergence

[x] ~Separate training for encoder+decoder and converter~

Notes (to be moved to README.md)

Multiple attention layers are hard to learn. Empirically, one or two (first and last) attention layers seems enough.

With guided attention (see https://arxiv.org/abs/1710.08969), alignments get monotonic more quickly and reliably if we use multiple attention layers. With guided attention, I can confirm five attention layers get monotonic, though I cannot get speech quality improvements.

Positional encoding (i.e., using text positions and frame positions in decoder) is essential to learn monotonic alignments (without this I cannot get it to work). However, I'm still not sure why position rate matters. 1.0 for both encoder/decoder worked from my previous experiment.

Weight initialization is quite important particularly for deeper (e.g. > 8 layers) networks. Noticed when I tried to replicate https://arxiv.org/abs/1710.08969. They use more than 20 layers in the decoder! Very hard to train. Work in progress in #3. Speech samples (model: encoder/converter from https://arxiv.org/abs/1710.08969 and decoder from DeepVoice3): https://www.dropbox.com/sh/q9xfgscgh3k5lqa/AACPgWCprBfNgjRravscdDYCa?dl=0.

Adam with step lr decay works. However, for deeper networks, I find Adam + noam's lr scheduler is more stable.
opened by r9y9 45
Tacotron 2

Sorry if this is off-topic (deepvoice vs tacotron) but it seems like the tacotron 2 paper is now released. The speech samples sounds better than ever (I think): https://google.github.io/tacotron/publications/tacotron2/index.html

I must admit that I'm not too well versed in how much this differs from the original tacotron. But perhaps the changes made also could be used in your projects?

opened by DarkDefender 41
Issue training with DeepVoice3 model with LJSpeech Data

Thanks for your excellent implementation of Deep Voice 3. I am attempting to retrain a DeepVoice3 model using the LJSpeech data. My interest in training a new model is that I want to make some small model parameter changes in order to enable fine-tuning using some Spanish data that I have.

As a first step I tried to retrain the baseline model and I have run into some issues.

With my installation, I have been able to successfully synthesize using the pre-trained DeepVoice3 model with git commit 4357976 as your instructions indicate. That synthesized audio sounds very much like the samples linked from the instructions page.

However, I am trying to train now with the latest git commit (commit 48d1014, dated Feb 7). I am using the LJSpeech data set downloaded from the link you provided. I have run the pre-processing and training steps as indicated in your instructions. I am using the default preset parameters for deepvoice3_ljspeech.

I have let the training process run for a while. When I synthesize using the checkpoint saved at 210K iterations, the alignment is bad and the audio is very robotic and mostly unintelligible.

When I synthesize using the checkpoint saved at 700K iterations, the alignment is better (but not great); the audio is improved but still robotic and choppy.

I can post the synthesized wav files via dropbox if you are interested. I expected to have good alignment and audio at 210K iterations as that is what the pretrained model used.

Any ideas what has changed between git commits 4357976 and 48d1014 that could have caused this issue? When I diff the two commits, I see some changes in audio.py, some places where support for multi-voice has been added, and some other changes I do not yet understand. There are some additions to hparams.py, but I only noticed one difference: in the current commit, masked_loss_weight defaults to 0.5, but in the prior commit the default was 0.0.

I have just started a new training run with masked_loss_weight set to 0.0. In the meantime, do you have thoughts on anything else that might be causing the issues I am seeing?
bug

opened by timbrucks 23
AttributeError: 'NoneType' object has no attribute 'text_to_sequence'

When I try to train a dataset with the command from the tutorial (python train.py --data-root=./data/ljspeech/ --hparams="builder=deepvoice3,preset=deepvoice3_ljspeech") I get an error telling me that _frontend is a NoneType object and has no 'text_to_sequence' attribute. Do I need to modify anything to get this to work again?

AttributeError: 'NoneType' object has no attribute 'text_to_sequence'
windows

opened by johnbie 22

KeyError: 'unexpected key "seq2seq.decoder.attention.in_projection.bias" in state_dict'

Hi, thanks for the fantastic DeepVoice3 implementation!

When trying to train Nyanko model starting from your pre-trained checkpoint using the following args:

--hparams="builder=nyanko,preset=nyanko_ljspeech" 
--checkpoint=checkpoints.pretrained/20171129_nyanko_checkpoint_step000585000.pth

I'm getting the error:

Load checkpoint from: checkpoints.pretrained/20171129_nyanko_checkpoint_step000585000.pth
Traceback (most recent call last):
  File "train.py", line 936, in <module>
    load_checkpoint(checkpoint_path, model, optimizer, reset_optimizer)
  File "train.py", line 820, in load_checkpoint
    model.load_state_dict(checkpoint["state_dict"])
  File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 490, in load_state_dict
    .format(name))
KeyError: 'unexpected key "seq2seq.decoder.attention.in_projection.bias" in state_dict'

Looks like in_projection is missing from AttentionLayer implementation in deepvoice3_pytorch/deepvoice3.py but still in the Nyanko pre-trained model https://github.com/r9y9/deepvoice3_pytorch#pretrained-models

wontfix

opened by nikitos9000 13

How to resume training? Also how to bias/weight the pronunciation to 2nd speaker?

This project of yours is AMAZING!

Thank you so much for offering this!

I have 370 of my own short (0-10 seconds) audio clips and transcriptions (totaling 15 minutes).

I'm running your program overnight right now to see if I can use LJSpeech 20180505_deepvoice3_checkpoint_step000640000.pth as a starting point that my own recordings would then build on top of.

If I want the resulting TTS voice to sound 100% like the voice of my new recordings and 0% like Linda Johnson, how can I do that?

I see the replace_pronunciation_prob variable in deepvoice3_ljspeech.json. Would setting it to 1.0 lead to the result I want?

Also, if my computer crashes or otherwise aborts training, how can I resume from where it left off?

(I found https://github.com/r9y9/deepvoice3_pytorch/blob/master/train.py#L15, but I'm not sure what that means or how to use it.)

Thank you so much :-)
wontfix

opened by ryancwalsh 12
Improving speaker adaptation with few voice samples

Hi, I tried adapting the pre-trained DeepVoice3 model to a dataset with only 23 voice samples (about 2 minutes) of only one speaker, using the LJSpeech preset. Does DeepVoice3 require more audio samples? After training for 1100 steps (about 4 hours on my system), it produced practically empty audio: Do I need more voice samples? Is there a rough figure for the same?
wontfix

opened by yrahul3910 12

positional encoding

    position_enc = np.array([
        [position_rate * pos / np.power(10000, 2 * (i // 2) / d_pos_vec) for i in range(d_pos_vec)]
        if pos != 0 else np.zeros(d_pos_vec) for pos in range(n_position)])

Hey! I wonder what is a motivation behind repeating positional encoding values twice? in paper it's done this way:

position_rate * pos / np.power(10000,  i / d_pos_vec)...

wontfix

opened by taras-sereda 12

TypeError: Cannot handle this data type

6it [00:09, 1.66s/it] Loss: 1.4190080761909485 4it [00:06, 1.57s/it]Traceback (most recent call last): File "/usr/lib/python3/dist-packages/PIL/Image.py", line 2150, in fromarray mode, rawmode = _fromarray_typemap[typekey] KeyError: ((1, 1, 75), '|u1')

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "https://t.co/oY6LXrEgZv", line 983, in train_seq2seq=train_seq2seq, train_postnet=train_postnet) File "https://t.co/oY6LXrEgZv", line 717, in train eval_model(global_step, writer, device, model, checkpoint_dir, ismultispeaker) File "https://t.co/oY6LXrEgZv", line 414, in eval_model writer.add_image(tag, np.uint8(cm.viridis(np.flip(alignment, 1).T) * 255), global_step) File "/usr/local/lib/python3.5/dist-packages/tensorboardX/writer.py", line 412, in add_image self.file_writer.add_summary(image(tag, img_tensor), global_step, walltime) File "/usr/local/lib/python3.5/dist-packages/tensorboardX/summary.py", line 205, in image image = make_image(tensor, rescale=rescale) File "/usr/local/lib/python3.5/dist-packages/tensorboardX/summary.py", line 243, in make_image image = Image.fromarray(tensor) File "/usr/lib/python3/dist-packages/PIL/Image.py", line 2153, in fromarray raise TypeError("Cannot handle this data type") TypeError: Cannot handle this data type

opened by usasho 10
Additional detail on using preprocess.py with gentle phoneme data

Hello @r9y9

Would you mind giving a few more details on what is needed to use preprocess.py as mentioned in the last step of the section on using custom data?

https://github.com/r9y9/deepvoice3_pytorch#1-2-preprocessing-custom-english-datasets-with-long-silence-based-on-vctk_preprocess

Initially I managed to train using custom data without using gentle, and the results were recognisably like my training data (recordings of my own voice) but I am hoping it will improve quality if I use gentle with the training data. I have managed to process the data with gentle_web_align.py, but I am unsure what parameters to use for preprocess.py now and also what format I need to put the files into.

Is there some similar format to the alignment.json file that should be created? And how would I incorporate the .lab files I got from gentle_web_align.py?

Sorry - I expect these may be obvious to you, but I've been trying to figure it out from looking over the code, but to no avail! :disappointed:

The project is really impressive - thank you for sharing your work!

Neil (@nmstoker)
wontfix

opened by nmstoker 9

Assertion `srcIndex < srcSelectDimSize` failed

Hi again,

I am applying this repository for Korean speech corpus (http://www.korean.go.kr/front/board/boardStandardView.do?board_id=4&mn_id=17&b_seq=464) and have encountered the following error. Could you have a look at it? I will be happy to ask PR once it gets working.

I formatted Korean corpus into npy as same as ljspeech has as single speaker and ran training with single GPU or multipe GPU. But it shows a series of error messages like Assertion srcIndex < srcSelectDimSize failed.

[[email protected] deepvoice3_pytorch]$ ls data/nikl | head -3
nikl-mel-00001.npy
nikl-mel-00002.npy
nikl-mel-00003.npy
[[email protected] deepvoice3_pytorch]$ ls data/nikl | tail -3
nikl-spec-00929.npy
nikl-spec-00930.npy
train.txt
[[email protected] deepvoice3_pytorch]$ ls data/nikl/*.npy | wc -l
1860


CUDA_VISIBLE_DEVICES=3 python train.py \
  --data-root=./data/nikl/ \
  --hparams="frontend=jp,builder=deepvoice3,preset=deepvoice3_ljspeech" \
  --checkpoint-dir checkpoint_nikl


Command line args:
 {'--checkpoint': None,
 '--checkpoint-dir': 'checkpoint_nikl',
 '--checkpoint-postnet': None,
 '--checkpoint-seq2seq': None,
 '--data-root': './data/nikl/',
 '--help': False,
 '--hparams': 'builder=deepvoice3,preset=deepvoice3_ljspeech',
 '--load-embedding': None,
 '--log-event-path': None,
 '--reset-optimizer': False,
 '--restore-parts': None,
 '--speaker-id': None,
 '--train-postnet-only': False,
 '--train-seq2seq-only': False}
Training whole model
Training seq2seq model
Hyperparameters:
  adam_beta1: 0.5
  adam_beta2: 0.9
  adam_eps: 1e-06
  allow_clipping_in_normalization: True
  batch_size: 16
  binary_divergence_weight: 0.1
  builder: deepvoice3
  checkpoint_interval: 10000
  clip_thresh: 0.1
  converter_channels: 256
  decoder_channels: 256
  downsample_step: 4
  dropout: 0.050000000000000044
  embedding_weight_std: 0.1
  encoder_channels: 256
  eval_interval: 10000
  fft_size: 1024
  force_monotonic_attention: True
  freeze_embedding: False
  frontend: en
  guided_attention_sigma: 0.2
  hop_size: 256
  initial_learning_rate: 0.0005
  kernel_size: 3
  key_position_rate: 1.385
  key_projection: False
  lr_schedule: noam_learning_rate_decay
  lr_schedule_kwargs: {}
  masked_loss_weight: 0.5
  max_positions: 512
  min_level_db: -100
  n_speakers: 1
  name: deepvoice3
  nepochs: 2000
  num_mels: 80
  num_workers: 2
  outputs_per_step: 1
  padding_idx: 0
  pin_memory: True
  power: 1.4
  preemphasis: 0.97
  preset: deepvoice3_ljspeech
  presets: {'deepvoice3_ljspeech': {'n_speakers': 1, 'downsample_step': 4, 'outputs_per_step': 1, 'embedding_weight_std': 0.1, 'dropout': 0.050000000000000044, 'kernel_size': 3, 'text_embed_dim': 256, 'enc
oder_channels': 512, 'decoder_channels': 256, 'converter_channels': 256, 'use_guided_attention': True, 'guided_attention_sigma': 0.2, 'binary_divergence_weight': 0.1, 'use_decoder_state_for_postnet_input':
 True, 'max_positions': 512, 'query_position_rate': 1.0, 'key_position_rate': 1.385, 'key_projection': True, 'value_projection': True, 'clip_thresh': 0.1, 'initial_learning_rate': 0.0005}, 'deepvoice3_vctk
': {'n_speakers': 108, 'speaker_embed_dim': 16, 'downsample_step': 4, 'outputs_per_step': 1, 'embedding_weight_std': 0.1, 'speaker_embedding_weight_std': 0.05, 'dropout': 0.050000000000000044, 'kernel_size
': 3, 'text_embed_dim': 256, 'encoder_channels': 512, 'decoder_channels': 256, 'converter_channels': 256, 'use_guided_attention': True, 'guided_attention_sigma': 0.4, 'binary_divergence_weight': 0.1, 'use_
decoder_state_for_postnet_input': True, 'max_positions': 1024, 'query_position_rate': 2.0, 'key_position_rate': 7.6, 'key_projection': True, 'value_projection': True, 'clip_thresh': 0.1, 'initial_learning_
rate': 0.0005}, 'nyanko_ljspeech': {'n_speakers': 1, 'downsample_step': 4, 'outputs_per_step': 1, 'embedding_weight_std': 0.01, 'dropout': 0.050000000000000044, 'kernel_size': 3, 'text_embed_dim': 128, 'en
coder_channels': 256, 'decoder_channels': 256, 'converter_channels': 256, 'use_guided_attention': True, 'guided_attention_sigma': 0.2, 'binary_divergence_weight': 0.1, 'use_decoder_state_for_postnet_input'
: True, 'max_positions': 512, 'query_position_rate': 1.0, 'key_position_rate': 1.385, 'key_projection': False, 'value_projection': False, 'clip_thresh': 0.1, 'initial_learning_rate': 0.0005}}
  priority_freq: 3000
  priority_freq_weight: 0.0
  query_position_rate: 1.0
  ref_level_db: 20
  replace_pronunciation_prob: 0.5
  sample_rate: 22050
  save_optimizer_state: True
  speaker_embed_dim: 16
  speaker_embedding_weight_std: 0.01
  text_embed_dim: 128
  trainable_positional_encodings: False
  use_decoder_state_for_postnet_input: True
  use_guided_attention: True
  use_memory_mask: True
  value_projection: False
  weight_decay: 0.0
  window_ahead: 3
  window_backward: 1
Override hyper parameters with preset "deepvoice3_ljspeech": {
    "n_speakers": 1,
    "downsample_step": 4,
    "outputs_per_step": 1,
    "embedding_weight_std": 0.1,
    "dropout": 0.050000000000000044,
    "kernel_size": 3,
    "text_embed_dim": 256,
    "encoder_channels": 512,
    "decoder_channels": 256,
    "converter_channels": 256,
    "use_guided_attention": true,
    "guided_attention_sigma": 0.2,
    "binary_divergence_weight": 0.1,
    "use_decoder_state_for_postnet_input": true,
    "max_positions": 512,
    "query_position_rate": 1.0,
    "key_position_rate": 1.385,
    "key_projection": true,
    "value_projection": true,
    "clip_thresh": 0.1,
    "initial_learning_rate": 0.0005
}
Los event path: log/run-test2018-01-30_15:05:32.238606
34it [00:08,  4.24it/s]
7it/s]/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, i
nt, IndexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [32,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [33,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [34,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [35,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [36,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [37,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [38,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [39,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [40,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [41,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [42,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [43,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [44,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [45,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [46,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [47,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [48,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [49,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [50,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [51,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [52,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [106,0,0], thread: [53,0,0] Assertion `srcIndex < srcSelectDimSize` failed.

...

/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [46,0,0], thread: [30,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/THCTensorIndex.cu:325: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, In
dexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 2, SrcDim = 2, IdxDim = -2]: block: [46,0,0], thread: [31,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/generic/THCStorage.cu line=58 error=59 : device-side assert triggered

Traceback (most recent call last):
  File "train.py", line 941, in <module>
    train_seq2seq=train_seq2seq, train_postnet=train_postnet)
  File "train.py", line 642, in train
    input_lengths=input_lengths)
  File "/home/kwon/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 325, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/kwon/3rdParty/deepvoice3_pytorch/deepvoice3_pytorch/__init__.py", line 94, in forward
    linear_outputs = self.postnet(postnet_inputs, speaker_embed)
  File "/home/kwon/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 325, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/kwon/3rdParty/deepvoice3_pytorch/deepvoice3_pytorch/deepvoice3.py", line 597, in forward
    return F.sigmoid(x)
  File "/home/kwon/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py", line 817, in sigmoid
    return input.sigmoid()
RuntimeError: cuda runtime error (59) : device-side assert triggered at /opt/conda/conda-bld/pytorch_1512386481460/work/torch/lib/THC/generic/THCStorage.cu:58

opened by homink 9

'SinusoidalEncoding' object has no attribute '_backend'

DeepVoice3 multi-speaker TTS en demo on Google Colab.

Generate Speech :

11 frames /usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in getattr(self, name) 1176 return modules[name] 1177 raise AttributeError("'{}' object has no attribute '{}'".format( -> 1178 type(self).name, name)) 1179 1180 def setattr(self, name: str, value: Union[Tensor, 'Module']) -> None: AttributeError: 'SinusoidalEncoding' object has no attribute '_backend'

opened by B1uee 0
train.py problem

Hi,

whenever I run train.py file using various parameters or path getting the below error. I am unable to understand the purpose of "train.txt". Please help

Command line args: {'--checkpoint': None, '--checkpoint-dir': 'checkpoints', '--checkpoint-postnet': None, '--checkpoint-seq2seq': None, '--data-root': './prepro', '--help': False, '--hparams': '', '--load-embedding': None, '--log-event-path': None, '--preset': 'presets/deepvoice3_ljspeech.json', '--reset-optimizer': False, '--restore-parts': None, '--speaker-id': None, '--train-postnet-only': False, '--train-seq2seq-only': False} Training whole model Training seq2seq model [!] Windows Detected - IF THAllocator.c 0x05 error occurs SET num_workers to 1 Hyperparameters: adam_beta1: 0.5 adam_beta2: 0.9 adam_eps: 1e-06 allow_clipping_in_normalization: True amsgrad: False batch_size: 16 binary_divergence_weight: 0.1 builder: deepvoice3 checkpoint_interval: 10000 clip_thresh: 0.1 converter_channels: 256 decoder_channels: 256 downsample_step: 4 dropout: 0.050000000000000044 embedding_weight_std: 0.1 encoder_channels: 512 eval_interval: 10000 fft_size: 1024 fmax: 7600 fmin: 125 force_monotonic_attention: True freeze_embedding: False frontend: en guided_attention_sigma: 0.2 hop_size: 256 ignore_recognition_level: 2 initial_learning_rate: 0.0005 kernel_size: 3 key_position_rate: 1.385 key_projection: True lr_schedule: noam_learning_rate_decay lr_schedule_kwargs: {} masked_loss_weight: 0.5 max_positions: 512 min_level_db: -100 min_text: 20 n_speakers: 1 name: deepvoice3 nepochs: 2000 num_mels: 80 num_workers: 2 outputs_per_step: 1 padding_idx: 0 pin_memory: True power: 1.4 preemphasis: 0.97 priority_freq: 3000 priority_freq_weight: 0.0 process_only_htk_aligned: False query_position_rate: 1.0 ref_level_db: 20 replace_pronunciation_prob: 0.5 rescaling: False rescaling_max: 0.999 sample_rate: 22050 save_optimizer_state: True speaker_embed_dim: 16 speaker_embedding_weight_std: 0.01 text_embed_dim: 256 trainable_positional_encodings: False use_decoder_state_for_postnet_input: True use_guided_attention: True use_memory_mask: True value_projection: True weight_decay: 0.0 window_ahead: 3 window_backward: 1 Traceback (most recent call last): File "train.py", line 954, in X = FileSourceDataset(TextDataSource(data_root, speaker_id)) File "C:\ProgramData\Anaconda3\envs\tf-gpu\lib\site-packages\nnmnkwii\datasets_init_.py", line 108, in init collected_files = self.file_data_source.collect_files() File "train.py", line 106, in collect_files with open(meta, "rb") as f: FileNotFoundError: [Errno 2] No such file or directory: './prepro\train.txt'

(tf-gpu) C:\Windows\System32\deepvoice3_pytorch>python train.py --preset=presets/deepvoice3_ljspeech.json --data-root=.\prepro Command line args: {'--checkpoint': None, '--checkpoint-dir': 'checkpoints', '--checkpoint-postnet': None, '--checkpoint-seq2seq': None, '--data-root': '.\prepro', '--help': False, '--hparams': '', '--load-embedding': None, '--log-event-path': None, '--preset': 'presets/deepvoice3_ljspeech.json', '--reset-optimizer': False, '--restore-parts': None, '--speaker-id': None, '--train-postnet-only': False, '--train-seq2seq-only': False} Training whole model Training seq2seq model [!] Windows Detected - IF THAllocator.c 0x05 error occurs SET num_workers to 1 Hyperparameters: adam_beta1: 0.5 adam_beta2: 0.9 adam_eps: 1e-06 allow_clipping_in_normalization: True amsgrad: False batch_size: 16 binary_divergence_weight: 0.1 builder: deepvoice3 checkpoint_interval: 10000 clip_thresh: 0.1 converter_channels: 256 decoder_channels: 256 downsample_step: 4 dropout: 0.050000000000000044 embedding_weight_std: 0.1 encoder_channels: 512 eval_interval: 10000 fft_size: 1024 fmax: 7600 fmin: 125 force_monotonic_attention: True freeze_embedding: False frontend: en guided_attention_sigma: 0.2 hop_size: 256 ignore_recognition_level: 2 initial_learning_rate: 0.0005 kernel_size: 3 key_position_rate: 1.385 key_projection: True lr_schedule: noam_learning_rate_decay lr_schedule_kwargs: {} masked_loss_weight: 0.5 max_positions: 512 min_level_db: -100 min_text: 20 n_speakers: 1 name: deepvoice3 nepochs: 2000 num_mels: 80 num_workers: 2 outputs_per_step: 1 padding_idx: 0 pin_memory: True power: 1.4 preemphasis: 0.97 priority_freq: 3000 priority_freq_weight: 0.0 process_only_htk_aligned: False query_position_rate: 1.0 ref_level_db: 20 replace_pronunciation_prob: 0.5 rescaling: False rescaling_max: 0.999 sample_rate: 22050 save_optimizer_state: True speaker_embed_dim: 16 speaker_embedding_weight_std: 0.01 text_embed_dim: 256 trainable_positional_encodings: False use_decoder_state_for_postnet_input: True use_guided_attention: True use_memory_mask: True value_projection: True weight_decay: 0.0 window_ahead: 3 window_backward: 1 Traceback (most recent call last): File "train.py", line 954, in X = FileSourceDataset(TextDataSource(data_root, speaker_id)) File "C:\ProgramData\Anaconda3\envs\tf-gpu\lib\site-packages\nnmnkwii\datasets_init_.py", line 108, in init collected_files = self.file_data_source.collect_files() File "train.py", line 106, in collect_files with open(meta, "rb") as f: FileNotFoundError: [Errno 2] No such file or directory: '.\prepro\train.txt'

opened by rabbia970 1
Installation nightmare

Wow this just boggles my mind how frigging crazy the whole installation process is for vctk preprocess https://github.com/r9y9/deepvoice3_pytorch/tree/master/vctk_preprocess

But when you finally get it installed, extract_feats.py just doesn't give a damn about all your ENVs, it just downloads SPTK every bloody time. Common people, this is just ridiculous, but thanks anyway.

opened by skol101 0
n_vocab AttributeError

Traceback (most recent call last): File "train.py", line 973, in model = build_model().to(device) File "train.py", line 816, in build_model n_vocab=_frontend.n_vocab, AttributeError: 'NoneType' object has no attribute 'n_vocab'

If you change the frontend of hparams.py to jp and run train.py, the error is output

opened by SODAsoo07 0
voice tone

Hello! Thank you for sharing your work! Would it be possible to make the voice with a certain tone or pitch? adapting it to a storytelling Thank you in advance!

opened by luantunez 0
Unknown hyperparameter type for use_preset

File "train.py", line 939, in hparams.parse(args["--hparams"]) File "/workspace/data/deepvoice3/deepvoice3_pytorch/deepvoice3_pytorch/tfcompat/hparam.py", line 543, in parse values_map = parse_values(values, type_map) File "/workspace/data/deepvoice3/deepvoice3_pytorch/deepvoice3_pytorch/tfcompat/hparam.py", line 263, in parse_values raise ValueError('Unknown hyperparameter type for %s' % name) ValueError: Unknown hyperparameter type for use_preset

opened by rishabh004-ai 1

Releases(v0.1.0)

v0.1.0(Oct 27, 2018)

First citable release. This should have a DOI for citation.
Source code(tar.gz)
Source code(zip)

PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

Related tags

Overview

Deepvoice3_pytorch

Folks

Online TTS demo

Highlights

Samples

Pretrained models

Notes on hyper parameters

Requirements

Installation

Getting started

Preset parameters

0. Download dataset

1. Preprocessing

1-1. Building custom dataset. (using json_meta)

1-2. Preprocessing custom english datasets with long silence. (Based on vctk_preprocess)

2. Training

NIKL

4. Monitor with Tensorboard

5. Synthesize from a checkpoint

Advanced usage

Multi-speaker model

VCTK

NIKL

Speaker adaptation

Trouble shooting

#5 RuntimeError: main thread is not in main loop

Sponsers

Acknowledgements

Comments

Single speaker model

Multi-speaker model

Misc

From https://arxiv.org/abs/1710.08969

Notes (to be moved to README.md)

Releases(v0.1.0)

v0.1.0(Oct 27, 2018)

Owner

Ryuichi Yamamoto

Semantic Segmentation in Pytorch

Pixel-wise segmentation on VOC2012 dataset using pytorch.

This repository contains the re-implementation of our paper deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling

No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency

CLIP: Connecting Text and Image (Learning Transferable Visual Models From Natural Language Supervision)

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

A pre-trained model with multi-exit transformer architecture.

Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python

Code implementation from my Medium blog post: [Transformers from Scratch in PyTorch]

Indoor Panorama Planar 3D Reconstruction via Divide and Conquer

Graph neural network message passing reframed as a Transformer with local attention

The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Advanced Signal Processing Notebooks and Tutorials

StocksMA is a package to facilitate access to financial and economic data of Moroccan stocks.

DEMix Layers for Modular Language Modeling

A embed able annotation tool for end to end cross document co-reference

The aim of this project is to build an AI bot that can play the Wordle game, or more generally Squabble

A general-purpose programming language, focused on simplicity, safety and stability.

Pytorch library for end-to-end transformer models training and serving

Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting