DeepSpeech German

This project is build upon the paper German End-to-end Speech Recognition based on DeepSpeech. Original paper code can be found here.

This project aims to develop a working Speech to Text module using Mozilla DeepSpeech, which can be used for any Audio processing pipeline. Mozillla DeepSpeech is a state-of-the-art open-source automatic speech recognition (ASR) toolkit. DeepSpeech is using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier.

Datasets

German Distant Speech Corpus (TUDA-De) ~185h
Mozilla Common Voice ~506h
Voxforge ~32h
Spoken Wikipedia Corpora (SWC) ~248h
M-AILABS Speech Dataset ~234h
GoogleWavenet ~165h, artificial training data generated with the google text to speech service
Tatoeba ~7h
CSS10 ~16h
Zamia-Speech ~19h
Noise data: Freesound Dataset Kaggle 2019 ~103h
Noise data: RNNoise ~44h
Noise data: Zamia-Noise ~5h

Links:

Forschergeist ~100-150h, no aligned transcriptions
Verbmobil + Others, seems to be paid only
Many different languages, most with login or non commercial
GerTV1000h German Broadcast corpus and Difficult Speech Corpus (DiSCo), no links found

Usage

General infos

File structure will look as follows:

my_deepspeech_folder
    checkpoints
    data_original
    data_prepared
    DeepSpeech
    deepspeech-german    <- This repository

Clone DeepSpeech and build container:

git clone https://github.com/mozilla/DeepSpeech.git
# or
git clone https://github.com/DanBmh/DeepSpeech.git
cd DeepSpeech && make Dockerfile.train && cd ..
docker build -f DeepSpeech/Dockerfile.train -t mozilla_deep_speech DeepSpeech/

Build and run our docker container:

docker build -t deep_speech_german deepspeech-german/
./deepspeech-german/run_container.sh

Download and prepare voice data

Download datasets (Run in docker container):

python3 deepspeech-german/preprocessing/download_data.py --tuda data_original/
python3 deepspeech-german/preprocessing/download_data.py --voxforge data_original/
python3 deepspeech-german/preprocessing/download_data.py --mailabs data_original/
python3 deepspeech-german/preprocessing/download_data.py --swc data_original/
python3 deepspeech-german/preprocessing/download_data.py --tatoeba data_original/
python3 deepspeech-german/preprocessing/download_data.py --common_voice data_original/
python3 deepspeech-german/preprocessing/download_data.py --zamia_speech data_original/

Download css10 german dataset (Requires kaggle account): LINK
Extract and move it to datasets directory (data_original/css_german/)
It seems the files are saved all twice, so remove the duplicate folders

Prepare datasets, this may take some time (Run in docker container):

# Prepare the datasets one by one to ensure everything is working:
python3 deepspeech-german/preprocessing/prepare_data.py --voxforge data_original/voxforge/ data_prepared/voxforge/
python3 deepspeech-german/preprocessing/prepare_data.py --tuda data_original/tuda/ data_prepared/tuda/
python3 deepspeech-german/preprocessing/prepare_data.py --common_voice data_original/common_voice/ data_prepared/common_voice/
python3 deepspeech-german/preprocessing/prepare_data.py --mailabs data_original/mailabs/ data_prepared/mailabs/
python3 deepspeech-german/preprocessing/prepare_data.py --swc data_original/swc/ data_prepared/swc/
python3 deepspeech-german/preprocessing/prepare_data.py --tatoeba data_original/tatoeba/ data_prepared/tatoeba/
python3 deepspeech-german/preprocessing/prepare_data.py --css_german data_original/css_german/ data_prepared/css_german/
python3 deepspeech-german/preprocessing/prepare_data.py --zamia_speech data_original/zamia_speech/ data_prepared/zamia_speech/
# To combine multiple datasets run the command as follows (not recommended):
python3 deepspeech-german/pre-processing/prepare_data.py --tuda data_original/tuda/ --voxforge data_original/voxforge/ data_prepared/tuda_voxforge/
# Or, which is much faster, but only combining train, dev, test and all csv files, run:
python3 deepspeech-german/preprocessing/combine_datasets.py data_prepared/ --tuda --voxforge
# Or to combine specific csv files:
python3 /DeepSpeech/deepspeech-german/preprocessing/combine_datasets.py "" --files_output /DeepSpeech/data_prepared/tvsmc/train_mix.csv --files "/DeepSpeech/data_prepared/tuda/train.csv /DeepSpeech/data_prepared/voxforge/train.csv /DeepSpeech/data_prepared/swc/all.csv /DeepSpeech/data_prepared/mailabs/all.csv /DeepSpeech/data_prepared/common_voice/train.csv"
# To shuffle and replace "äöü" characters and clean the files run (repeat for all 3 csv files):
python3 /DeepSpeech/deepspeech-german/preprocessing/dataset_operations.py /DeepSpeech/data_prepared/voxforge/train.csv /DeepSpeech/data_prepared/voxforge/train_azce.csv --replace --shuffle --clean --exclude
# To split tuda into the correct train, dev and test splits run: 
# (you will have to rename the [train/dev/test]_s.csv files before combining them with other datasets)
python3 deepspeech-german/preprocessing/split_dataset.py data_prepared/tuda/all.csv --tuda --file_appendix _s

Preparation times using Intel i7-8700K:

voxforge: some seconds
tuda: some minutes
mailabs: ~20min
common_voice: ~12h
swc: ~6h

Download and prepare noise data

You have to merge mozilla/DeepSpeech#2622 for testing with noise, or use my noiseaugmaster branch.
Run in container:

cd data_original/noise/
# Download freesound data:
wget https://zenodo.org/record/3612637/files/FSDKaggle2019.audio_test.zip?download=1 -O FSDKaggle2019.audio_test.zip
wget https://zenodo.org/record/3612637/files/FSDKaggle2019.audio_train_curated.zip?download=1 -O FSDKaggle2019.audio_train_curated.zip
wget https://zenodo.org/record/3612637/files/FSDKaggle2019.audio_train_noisy.z01?download=1 -O FSDKaggle2019.audio_train_noisy.z01
wget https://zenodo.org/record/3612637/files/FSDKaggle2019.audio_train_noisy.z02?download=1 -O FSDKaggle2019.audio_train_noisy.z02
wget https://zenodo.org/record/3612637/files/FSDKaggle2019.audio_train_noisy.z03?download=1 -O FSDKaggle2019.audio_train_noisy.z03
wget https://zenodo.org/record/3612637/files/FSDKaggle2019.audio_train_noisy.z04?download=1 -O FSDKaggle2019.audio_train_noisy.z04
wget https://zenodo.org/record/3612637/files/FSDKaggle2019.audio_train_noisy.z05?download=1 -O FSDKaggle2019.audio_train_noisy.z05
wget https://zenodo.org/record/3612637/files/FSDKaggle2019.audio_train_noisy.z06?download=1 -O FSDKaggle2019.audio_train_noisy.z06
wget https://zenodo.org/record/3612637/files/FSDKaggle2019.audio_train_noisy.zip?download=1 -O FSDKaggle2019.audio_train_noisy.zip
# Merge the seven parts:
zip -s 0 FSDKaggle2019.audio_train_noisy.zip --out unsplit.zip
unzip FSDKaggle2019.audio_test.zip
unzip FSDKaggle2019.audio_train_curated.zip
unzip unsplit.zip
rm *.zip
rm *.z0*
# Download rnnoise data:
wget https://media.xiph.org/rnnoise/rnnoise_contributions.tar.gz
tar -xvzf rnnoise_contributions.tar.gz
rm rnnoise_contributions.tar.gz
# Download zamia noise
wget http://goofy.zamia.org/zamia-speech/corpora/noise.tar.xz
tar -xvf noise.tar.xz
mv noise/ zamia/
rm noise.tar.xz
# Normalize all the audio files (run with python2):
python /DeepSpeech/deepspeech-german/preprocessing/normalize_noise_audio.py --from_dir /DeepSpeech/data_original/noise/ --to_dir /DeepSpeech/data_prepared/noise/ --max_sec 45
# Create csv files:
python3 /DeepSpeech/deepspeech-german/preprocessing/noise_to_csv.py
python3 /DeepSpeech/deepspeech-german/preprocessing/split_dataset.py /DeepSpeech/data_prepared/noise/all.csv  --split "70|15|15"

Create the language model

Download and prepare the text corpora tuda, europarl+news:

cd data_original/texts/
wget "http://ltdata1.informatik.uni-hamburg.de/kaldi_tuda_de/German_sentences_8mil_filtered_maryfied.txt.gz" -O tuda_sentences.txt.gz
gzip -d tuda_sentences.txt.gz
wget "https://www.statmt.org/wmt13/training-monolingual-nc-v8.tgz" -O news-commentary.tgz
tar zxvf news-commentary.tgz && mv training/news-commentary-v8.de news-commentary-v8.de && rm news-commentary.tgz && rm -r training/
# If you have enough space you can also download the other years
wget "https://www.statmt.org/wmt13/training-monolingual-news-2012.tgz" -O news-2012.tgz
tar zxvf news-2012.tgz && mv training-monolingual/news.2012.de.shuffled news.2012.de && rm news-2012.tgz && rm -r training-monolingual/
wget "https://www.statmt.org/wmt13/training-monolingual-europarl-v7.tgz" -O europarl.tgz
tar zxvf europarl.tgz && mv training/europarl-v7.de europarl-v7.de && rm europarl.tgz && rm -r training/
# This needs a lot of memory for processing (~30gb, but you can also skip some of the files)
python3 /DeepSpeech/deepspeech-german/preprocessing/prepare_vocab.py /DeepSpeech/data_original/texts/ /DeepSpeech/data_prepared/clean_vocab_az.txt --replace_umlauts

Generate scorer (Run in docker container):

mkdir data_prepared/lm/
python3 /DeepSpeech/data/lm/generate_lm.py --input_txt /DeepSpeech/data_prepared/clean_vocab_az.txt --output_dir /DeepSpeech/data_prepared/lm/ --top_k 500000 --kenlm_bins /DeepSpeech/native_client/kenlm/build/bin/ --arpa_order 5 --max_arpa_memory "85%" --arpa_prune "0|0|1" --binary_a_bits 255 --binary_q_bits 8 --binary_type trie
python3 /DeepSpeech/data/lm/generate_package.py --alphabet /DeepSpeech/deepspeech-german/data/alphabet_az.txt --lm /DeepSpeech/data_prepared/lm/lm.binary --vocab /DeepSpeech/data_prepared/lm/vocab-500000.txt --package /DeepSpeech/data_prepared/lm/kenlm_az.scorer --default_alpha 0.75 --default_beta 1.85

Fix some issues

For me only training with voxforge worked at first. With tuda dataset I got an error:
"Invalid argument: Not enough time for target transition sequence"

To fix it you have to follow this solution:

# Add the parameter "ignore_longer_outputs_than_inputs=True" in DeepSpeech.py (~ line 231)
# Compute the CTC loss using TensorFlow's `ctc_loss`
total_loss = tfv1.nn.ctc_loss(labels=batch_y, inputs=logits, sequence_length=batch_seq_len, ignore_longer_outputs_than_inputs=True)

This will result in another error after some training steps:
"Invalid argument: WAV data chunk '[Some strange symbol here]"

Just ignore this in the train steps:

# Add another exception (tf.errors.InvalidArgumentError) in the training loop in DeepSpeech.py (~ line 602):
try:
    [...]
    session.run([train_op, global_step, loss, non_finite_files, step_summaries_op], feed_dict=feed_dict)
except tf.errors.OutOfRangeError:
    break
except tf.errors.InvalidArgumentError as e:
    print("Ignoring error:", e)
    continue

Add the parameter and the ignored exception in evaluate.py file too (~ lines 73 and 118).

To filter the files causing infinite loss:

# Below this lines (DeepSpeech.py ~ line 620):
problem_files = [f.decode('utf8') for f in problem_files[..., 0]]
log_error('The following files caused an infinite (or NaN) '
          'loss: {}'.format(','.join(problem_files)))
# Add the following to save the files to excluded_files.json and stop training
sys.path.append("/DeepSpeech/deepspeech-german/training/")
from filter_invalid_files import add_files_to_excluded
add_files_to_excluded(problem_files)
sys.exit(1)

Training

Download pretrained deepspeech checkpoints.

wget https://github.com/mozilla/DeepSpeech/releases/download/v0.7.3/deepspeech-0.7.3-checkpoint.tar.gz -P checkpoints/
tar xvfz checkpoints/deepspeech-0.7.3-checkpoint.tar.gz -C checkpoints/
rm checkpoints/deepspeech-0.7.3-checkpoint.tar.gz

Adjust the parameters to your needs (Run in docker container):

# Delete old model files:
rm -rf /root/.local/share/deepspeech/summaries && rm -rf /root/.local/share/deepspeech/checkpoints
# Run training:
python3 DeepSpeech.py --train_files data_prepared/voxforge/train.csv --dev_files data_prepared/voxforge/dev.csv --test_files data_prepared/voxforge/test.csv \
--alphabet_config_path deepspeech-german/data/alphabet.txt --lm_trie_path data_prepared/trie --lm_binary_path data_prepared/lm.binary --test_batch_size 48 --train_batch_size 48 --dev_batch_size 48 \
--epochs 75 --learning_rate 0.0005 --dropout_rate 0.40 --export_dir deepspeech-german/models --use_allow_growth --use_cudnn_rnn 
# Or adjust the train.sh file and run a training using the english checkpoint:
/bin/bash /DeepSpeech/deepspeech-german/training/train.sh /DeepSpeech/checkpoints/voxforge/ /DeepSpeech/data_prepared/voxforge/train_azce.csv /DeepSpeech/data_prepared/voxforge/dev_azce.csv /DeepSpeech/data_prepared/voxforge/test_azce.csv 1 /DeepSpeech/checkpoints/deepspeech-0.6.0-checkpoint/
# Or to run a cycled training as described in the paper, run:
python3 /DeepSpeech/deepspeech-german/training/cycled_training.py /DeepSpeech/checkpoints/voxforge/ /DeepSpeech/data_prepared/ _azce --voxforge
# Run test only (The use_allow_growth flag fixes "cuDNN failed to initialize" error):
# Don't forget to add the noise augmentation flags if testing with noise
python3 /DeepSpeech/DeepSpeech.py --test_files /DeepSpeech/data_prepared/voxforge/test_azce.csv --checkpoint_dir /DeepSpeech/checkpoints/voxforge/ --scorer_path /DeepSpeech/data_prepared/lm/kenlm_az.scorer --alphabet_config_path /DeepSpeech/deepspeech-german/data/alphabet_az.txt --test_batch_size 36 --use_allow_growth

Training time for voxforge on 2x Nvidia 1080Ti using batch size of 48 is about 01:45min per epoch. Training until early stop took 22min for 10 epochs.

One epoch in tuda with batch size of 12 on single gpu needs about 1:15h. With both gpus it takes about 26min. For 10 cycled training with early stops it took about 15h.

One epoch in mailabs with batch size of 24/12/12 needs about 19min, testing about 21 min.

One epoch in swc with batch size of 12/12/12 needs about 1:08h, testing about 17 min.

One epoch with all datasets and batch size of 12 needs about 2:50h, testing about 1:30h. Training until early stop took 37h for 11 epochs.

One epoch with all datasets and only Tuda + CommonVoice as testset needs about 3:30h. Training for 55 epochs took 8d 6h, testing about 1h.

Results

Paper

Some results from the findings in the paper German End-to-end Speech Recognition based on DeepSpeech:

Mozilla 79.7%
Voxforge 72.1%
Tuda-De 26.8%
Tuda-De+Mozilla 57.3%
Tuda-De+Voxforge 15.1%
Tuda-De+Voxforge+Mozilla 21.5%

To test their uploaded checkpoint you have to add a file best_dev_checkpoint next to the checkpoint files. Insert following content:

model_checkpoint_path: "best_dev-22218"
all_model_checkpoint_paths: "best_dev-22218"

Switch the DeepSpeech repository back to tag v0.5.0 and build a new docker image. Don't forget to fix the above issue in evaluate.py again. Mount the checkpoint and data directories and run:

python3 /DeepSpeech/DeepSpeech.py --test_files /DeepSpeech/data_prepared/voxforge/test_azce.csv --checkpoint_dir /DeepSpeech/checkpoints/dsg05_models/checkpoints/ \
--alphabet_config_path /DeepSpeech/checkpoints/dsg05_models/alphabet.txt --lm_trie_path /DeepSpeech/checkpoints/dsg05_models/trie --lm_binary_path /DeepSpeech/checkpoints/dsg05_models/lm.binary --test_batch_size 48

Dataset	Additional Infos	Losses	Result
Tuda + CommonVoice	used newer CommonVoice version, there may be overlaps between test and training data because of random splitting	Test: 105.747589	WER: 0.683802, CER: 0.386331
Tuda	correct tuda test split, there may be overlaps between test and training data because of random splitting	Test: 402.696991	WER: 0.785655, CER: 0.428786

This repo

Some results with a old code version (Default dropout is 0.4, learning rate 0.0005):

Dataset	Additional Infos	Result
Voxforge		WER: 0.676611, CER: 0.403916, loss: 82.185226
Voxforge	with augmentation	WER: 0.624573, CER: 0.348618, loss: 74.403786
Voxforge	without "äöü"	WER: 0.646702, CER: 0.364471, loss: 82.567413
Voxforge	cleaned data, without "äöü"	WER: 0.634828, CER: 0.353037, loss: 81.905258
Voxforge	above checkpoint, tested on not cleaned data	WER: 0.634556, CER: 0.352879, loss: 81.849220
Voxforge	checkpoint from english deepspeech, without "äöü"	WER: 0.394064, CER: 0.190184, loss: 49.066357
Voxforge	checkpoint from english deepspeech, with augmentation, without "äöü", dropout 0.25, learning rate 0.0001	WER: 0.338685, CER: 0.150972, loss: 42.031754
Voxforge	reduce learning rate on plateau, with noise and standard augmentation, checkpoint from english deepspeech, cleaned data, without "äöü", dropout 0.25, learning rate 0.0001, batch size 48	WER: 0.320507, CER: 0.131948, loss: 39.923031
Voxforge	above with learning rate 0.00001	WER: 0.350903, CER: 0.147837, loss: 43.451263
Voxforge	above with learning rate 0.001	WER: 0.518670, CER: 0.252510, loss: 62.927200
Tuda + Voxforge	without "äöü", checkpoint from english deepspeech, cleaned train and dev data	WER: 0.740130, CER: 0.462036, loss: 156.115921
Tuda + Voxforge	first Tuda then Voxforge, without "äöü", cleaned train and dev data, dropout 0.25, learning rate 0.0001	WER: 0.653841, CER: 0.384577, loss: 159.509476
Tuda + Voxforge + SWC + Mailabs + CommonVoice	checkpoint from english deepspeech, with augmentation, without "äöü", cleaned data, dropout 0.25, learning rate 0.0001	WER: 0.306061, CER: 0.151266, loss: 33.218510

Some results with some older code version:
(Default values: batch size 12, dropout 0.25, learning rate 0.0001, without "äöü", cleaned data , checkpoint from english deepspeech, early stopping, reduce learning rate on plateau, evaluation with scorer and top-500k words)

Dataset	Additional Infos	Losses	Training epochs of best model	Result
Tuda + Voxforge + SWC + Mailabs + CommonVoice	test only with Tuda + CommonVoice others completely for training, language model with training transcriptions, with augmentation	Test: 29.363405, Validation: 23.509546	55	WER: 0.190189, CER: 0.091737
Tuda + Voxforge + SWC + Mailabs + CommonVoice	above checkpoint tested with 3-gram language model	Test: 29.363405		WER: 0.199709, CER: 0.095318
Tuda + Voxforge + SWC + Mailabs + CommonVoice	above checkpoint tested on Tuda only	Test: 87.074394		WER: 0.378379, CER: 0.167380

Some results with the current code version:
(Default values: batch size 36, dropout 0.25, learning rate 0.0001, without "äöü", cleaned data , checkpoint from english deepspeech, early stopping, reduce learning rate on plateau, evaluation with scorer and top-500k words, data augmentation)

Dataset	Additional Infos	Losses	Training epochs of best model	Result
Voxforge	training from scratch	Test: 79.124008, Validation: 81.982976	29	WER: 0.603879, CER: 0.298139
Voxforge		Test: 44.312195, Validation: 47.915317	21	WER: 0.343973, CER: 0.140119
Voxforge	without reduce learning rate on plateau	Test: 46.160049, Validation: 48.926518	13	WER: 0.367125, CER: 0.163931
Voxforge	dropped last layer	Test: 49.844028, Validation: 52.722362	21	WER: 0.389327, CER: 0.170563
Voxforge	5 cycled training	Test: 42.973358		WER: 0.353841, CER: 0.158554

Tuda	training from scratch, correct train/dev/test splitting	Test: 149.653427, Validation: 137.645307	9	WER: 0.606629, CER: 0.296630
Tuda	correct train/dev/test splitting	Test: 103.179092, Validation: 132.243965	3	WER: 0.436074, CER: 0.208135
Tuda	dropped last layer, correct train/dev/test splitting	Test: 107.047821, Validation: 101.219325	6	WER: 0.431361, CER: 0.195361
Tuda	dropped last two layers, correct train/dev/test splitting	Test: 110.523621, Validation: 103.844562	5	WER: 0.442421, CER: 0.204504
Tuda	checkpoint from Voxforge with WER 0.344, correct train/dev/test splitting	Test: 100.846367, Validation: 95.410456	3	WER: 0.416950, CER: 0.198177
Tuda	10 cycled training, checkpoint from Voxforge with WER 0.344, correct train/dev/test splitting	Test: 98.007607		WER: 0.410520, CER: 0.194091
Tuda	random dataset splitting, checkpoint from Voxforge with WER 0.344 Important Note: These results are not meaningful, because same transcriptions can occur in train and test set, only recorded with different microphones	Test: 23.322618, Validation: 23.094230	27	WER: 0.090285, CER: 0.036212

CommonVoice	checkpoint from Tuda with WER 0.417	Test: 24.688297, Validation: 17.460029	35	WER: 0.217124, CER: 0.085427
CommonVoice	above tested with reduced testset where transcripts occurring in trainset were removed,	Test: 33.376812		WER: 0.211668, CER: 0.079157
CommonVoice + GoogleWavenet	above tested with GoogleWavenet	Test: 17.653290		WER: 0.035807, CER: 0.007342
CommonVoice	checkpoint from Voxforge with WER 0.344	Test: 23.460932, Validation: 16.641201	35	WER: 0.215584, CER: 0.084932
CommonVoice	dropped last layer	Test: 24.480028, Validation: 17.505738	36	WER: 0.220435, CER: 0.086921

Tuda + GoogleWavenet	added GoogleWavenet to train data, dev/test from Tuda, checkpoint from Voxforge with WER 0.344	Test: 95.555939, Validation: 90.392490	3	WER: 0.390291, CER: 0.178549
Tuda + GoogleWavenet	GoogleWavenet as train data, dev/test from Tuda	Test: 346.486420, Validation: 326.615474	0	WER: 0.865683, CER: 0.517528
Tuda + GoogleWavenet	GoogleWavenet as train/dev data, test from Tuda	Test: 477.049591, Validation: 3.320163	23	WER: 0.923973, CER: 0.601015
Tuda + GoogleWavenet	above checkpoint tested with GoogleWavenet	Test: 3.406022		WER: 0.012919, CER: 0.001724
Tuda + GoogleWavenet	checkpoint from english deepspeech tested with Tuda	Test: 402.102661		WER: 0.985554, CER: 0.752787
Voxforge + GoogleWavenet	added all of GoogleWavenet to train data, dev/test from Voxforge	Test: 45.643063, Validation: 49.620488	28	WER: 0.349552, CER: 0.143108
CommonVoice + GoogleWavenet	added all of GoogleWavenet to train data, dev/test from CommonVoice	Test: 25.029057, Validation: 17.511973	35	WER: 0.214689, CER: 0.084206
CommonVoice + GoogleWavenet	above tested with reduced testset	Test: 34.191067		WER: 0.213164, CER: 0.079121

Updated to DeepSpeech v0.7.3 and new english checkpoint:
(Testing with noise and speech overlay is done with older noiseaugmaster branch, which implemented this functionality)

Dataset	Additional Infos	Losses	Training epochs of best model	Result
Voxforge		Test: 32.844025, Validation: 36.912005	14	WER: 0.240091, CER: 0.087971
Voxforge	without freq_and_time_masking augmentation	Test: 33.698494, Validation: 38.071722	10	WER: 0.244600, CER: 0.094577
Voxforge	using new audio augmentation options	Test: 29.280865, Validation: 33.294815	21	WER: 0.220538, CER: 0.079463

Voxforge	updated augmentations again	Test: 28.846869, Validation: 32.680268	16	WER: 0.225360, CER: 0.083504
Voxforge	test above with older noiseaugmaster branch	Test: 28.831675		WER: 0.238961, CER: 0.081555
Voxforge	test with speech overlay	Test: 89.661995		WER: 0.570903, CER: 0.301745
Voxforge	test with noise overlay	Test: 53.461609		WER: 0.438126, CER: 0.213890
Voxforge	test with speech and noise overlay	Test: 79.736122		WER: 0.581259, CER: 0.310365
Voxforge	second test with speech and noise to check random influence	Test: 81.241333		WER: 0.595410, CER: 0.319077

Voxforge	add speech overlay augmentation	Test: 28.843914, Validation: 32.341234	27	WER: 0.222024, CER: 0.083036
Voxforge	change snr=50:20~~9m to snr=30:15~~9	Test: 28.502413, Validation: 32.236247	28	WER: 0.226005, CER: 0.085475
Voxforge	test above with older noiseaugmaster branch	Test: 28.488537		WER: 0.239530, CER: 0.083855
Voxforge	test with speech overlay	Test: 47.783081		WER: 0.383612, CER: 0.175735
Voxforge	test with noise overlay	Test: 51.682060		WER: 0.428566, CER: 0.209789
Voxforge	test with speech and noise overlay	Test: 60.275940		WER: 0.487709, CER: 0.255167

Voxforge	add noise overlay augmentation	Test: 27.940659, Validation: 31.988175	28	WER: 0.219143, CER: 0.076050
Voxforge	change snr=50:20~~6 to snr=24:12~~6	Test: 26.588453, Validation: 31.151855	34	WER: 0.206141, CER: 0.072018
Voxforge	change to snr=18:9~6	Test: 26.311581, Validation: 30.531299	30	WER: 0.211865, CER: 0.074281
Voxforge	test above with older noiseaugmaster branch	Test: 26.300938		WER: 0.227466, CER: 0.073827
Voxforge	test with speech overlay	Test: 76.401451		WER: 0.499962, CER: 0.254203
Voxforge	test with noise overlay	Test: 44.011471		WER: 0.376783, CER: 0.165329
Voxforge	test with speech and noise overlay	Test: 65.408264		WER: 0.496168, CER: 0.246516

Voxforge	speech and noise overlay	Test: 27.101889, Validation: 31.407527	44	WER: 0.220243, CER: 0.082179
Voxforge	test above with older noiseaugmaster branch	Test: 27.087360		WER: 0.232094, CER: 0.080319
Voxforge	test with speech overlay	Test: 46.012951		WER: 0.362291, CER: 0.164134
Voxforge	test with noise overlay	Test: 44.035809		WER: 0.377276, CER: 0.171528
Voxforge	test with speech and noise overlay	Test: 53.832214		WER: 0.441768, CER: 0.218798

Tuda + Voxforge + SWC + Mailabs + CommonVoice	test with Voxforge + Tuda + CommonVoice others completely for training, with noise and speech overlay	Test: 22.055849, Validation: 17.613633	46	WER: 0.208809, CER: 0.087215

Language Model and Checkpoints

Scorer with training transcriptions: Link
Checkpoints TVSMC training with 0.19 WER: Link
Graph model for above checkpoint: Link

Name		Name	Last commit message	Last commit date
Latest commit History 165 Commits
data		data
media		media
preprocessing		preprocessing
slurm		slurm
training		training
.dockerignore		.dockerignore
.gitignore		.gitignore
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
run_container.sh		run_container.sh

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lexkoro/deepspeech-german

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