
## Speechcatcher ESPnet streaming ASR model XL for German ASR

### `speechcatcher/speechcatcher_german_espnet_streaming_transformer_26k_train_size_xl_raw_de_bpe1024`

This model was trained by bmilde using the Speechcatcher recipe in [espnet](https://github.com/speechcatcher-asr/espnet/tree/egs2-speechcatcher-de).

### Demo: How to use the model

Global installation:

```bash

sudo apt-get install portaudio19-dev python3.10-dev ffmpeg
# on mac:
# brew install portaudio ffmpeg
pip3 install git+https://github.com/speechcatcher-asr/speechcatcher
speechcatcher -m de_streaming_transformer_xl mediafile.mp4
# or with a microphone:
speechcatcher -m de_streaming_transformer_xl -l
```

Virtual environment:

```bash
virtualenv -p python3.10 speechcatcher_env
source speechcatcher_env/bin/activate
pip3 install git+https://github.com/speechcatcher-asr/speechcatcher
speechcatcher -m de_streaming_transformer_xl mediafile.mp4
# or with a microphone:
speechcatcher -m de_streaming_transformer_xl -l
```

# RESULTS

Tuda-de-raw: 2.4% CER (without LM)

Tuda-de-raw: 8.5% WER (without LM)

Note: Tuda-de-raw results are based on raw tuda-de test utterances without the normalization step. It may not be directly comparable to regular tuda-de results.

# Speechcatcher training

Speechcatcher models are trained by using Whisper large as a teacher model:

![Speechcatcher Teacher/student training](https://github.com/speechcatcher-asr/speechcatcher/raw/main/speechcatcher_training.svg)

See [speechcatcher-data](https://github.com/speechcatcher-asr/speechcatcher-data) for code and more info on replicating the training process.

# Sponsors

Speechcatcher was gracefully funded by

<a href="https://media-tech-lab.com">Media Tech Lab by Media Lab Bayern</a> (<a href="https://github.com/media-tech-lab">@media-tech-lab</a>)

<a href="https://media-tech-lab.com">
    <img src="https://raw.githubusercontent.com/media-tech-lab/.github/main/assets/mtl-powered-by.png" width="240" title="Media Tech Lab powered by logo">
</a>

# Citing

```BibTex
@misc{milde2023speechcatcher,
  author = {Milde, Benjamin},
  title = {Speechcatcher},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/speechcatcher-asr/speechcatcher}},
}
```

# ASR config

<details><summary>expand</summary>

```
config: conf/train_asr_streaming_transformer_size_xl.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_streaming_transformer_size_xl_raw_de_bpe1024
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 58047
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 16
patience: 3
val_scheduler_criterion:
- valid
- acc
early_stopping_criterion:
- valid
- acc
- max
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 6
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 32
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_de_bpe1024/train/speech_shape
- exp/asr_stats_raw_de_bpe1024/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_de_bpe1024/valid/speech_shape
- exp/asr_stats_raw_de_bpe1024/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/train/wav.scp
    - speech
    - sound
-   - dump/raw/train/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/dev/wav.scp
    - speech
    - sound
-   - dump/raw/dev/text
    - text
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
    lr: 0.001
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ','
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- ▁
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- «
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- à
- '*'
- q
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- è
- ş
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- á
- ë
- ç
- —
- í
- °
- /
- å
- $
- č
- ô
- š
- ó
- _
- ñ
- ı
- õ
- '"'
- ›
- ́
- ‹
- ⁄
- ê
- û
- É
- ':'
- ğ
- Ã
- )
- ø
- ♪
- ã
- ł
- Ş
- ù
- ž
- î
- â
- ń
- Č
- ï
- ā
- Š
- ș
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- ę
- ś
- ō
- ð
- İ
- ă
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- ò
- ř
- Ø
- ý
- ì
- ē
- ̄
- μ
- ż
- ·
- À
- Á
- ο
- ṇ
- ě
- ;
- ^
- Î
- ą
- о
- ה
- ♫
- 스
- 해
- §
- λ
- τ
- Ó
- đ
- Ž
- ț
- '>'
- '~'
- £
- Å
- ź
- Ż
- Ȗ
- Ε
- Π
- έ
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init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: null
    zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: bpe
bpemodel: data/de_token_list/bpe_unigram1024/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
frontend: default
frontend_conf:
    n_fft: 512
    win_length: 400
    hop_length: 160
    fs: 16k
specaug: specaug
specaug_conf:
    apply_time_warp: true
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 30
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 40
    num_time_mask: 2
normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_raw_de_bpe1024/train/feats_stats.npz
model: espnet
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: contextual_block_transformer
encoder_conf:
    output_size: 256
    attention_heads: 8
    linear_units: 2048
    num_blocks: 30
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.0
    input_layer: conv2d
    normalize_before: true
    block_size: 40
    hop_size: 16
    look_ahead: 16
    init_average: true
    ctx_pos_enc: true
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
    attention_heads: 8
    linear_units: 2048
    num_blocks: 14
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.0
    src_attention_dropout_rate: 0.0
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202211'
distributed: true
```

</details>
{"language": "de", "license": "mit", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["speechcatcher"]}
espnet audio automatic-speech-recognition de dataset:speechcatcher license:mit region:us
TAGS
#espnet #audio #automatic-speech-recognition #de #dataset-speechcatcher #license-mit #region-us 


## Speechcatcher ESPnet streaming ASR model XL for German ASR

### 'speechcatcher/speechcatcher_german_espnet_streaming_transformer_26k_train_size_xl_raw_de_bpe1024'

This model was trained by bmilde using the Speechcatcher recipe in espnet.

### Demo: How to use the model

Global installation:



Virtual environment:



# RESULTS

Tuda-de-raw: 2.4% CER (without LM)

Tuda-de-raw: 8.5% WER (without LM)

Note: Tuda-de-raw results are based on raw tuda-de test utterances without the normalization step. It may not be directly comparable to regular tuda-de results.

# Speechcatcher training

Speechcatcher models are trained by using Whisper large as a teacher model:

!Speechcatcher Teacher/student training

See speechcatcher-data for code and more info on replicating the training process.

# Sponsors

Speechcatcher was gracefully funded by

<a href="URL">Media Tech Lab by Media Lab Bayern</a> (<a href="URL

<a href="URL">
    <img src="URL width="240" title="Media Tech Lab powered by logo">
</a>

# Citing



# ASR config

<details><summary>expand</summary>



</details>
## Speechcatcher ESPnet streaming ASR model XL for German ASR ### 'speechcatcher/speechcatcher_german_espnet_streaming_transformer_26k_train_size_xl_raw_de_bpe1024'

This model was trained by bmilde using the Speechcatcher recipe in espnet. ### Demo: How to use the model

Global installation:



Virtual environment: # RESULTS

Tuda-de-raw: 2.4% CER (without LM)

Tuda-de-raw: 8.5% WER (without LM)

Note: Tuda-de-raw results are based on raw tuda-de test utterances without the normalization step. It may not be directly comparable to regular tuda-de results. # Speechcatcher training

Speechcatcher models are trained by using Whisper large as a teacher model:

!Speechcatcher Teacher/student training

See speechcatcher-data for code and more info on replicating the training process. # Sponsors

Speechcatcher was gracefully funded by

<a href="URL">Media Tech Lab by Media Lab Bayern</a> (<a href="URL

<a href="URL">
    <img src="URL width="240" title="Media Tech Lab powered by logo">
</a> # Citing # ASR config

<details><summary>expand</summary>



</details>
TAGS
#espnet #audio #automatic-speech-recognition #de #dataset-speechcatcher #license-mit #region-us 
 ## Speechcatcher ESPnet streaming ASR model XL for German ASR ### 'speechcatcher/speechcatcher_german_espnet_streaming_transformer_26k_train_size_xl_raw_de_bpe1024'

This model was trained by bmilde using the Speechcatcher recipe in espnet. ### Demo: How to use the model

Global installation:



Virtual environment: # RESULTS

Tuda-de-raw: 2.4% CER (without LM)

Tuda-de-raw: 8.5% WER (without LM)

Note: Tuda-de-raw results are based on raw tuda-de test utterances without the normalization step. It may not be directly comparable to regular tuda-de results. # Speechcatcher training

Speechcatcher models are trained by using Whisper large as a teacher model:

!Speechcatcher Teacher/student training

See speechcatcher-data for code and more info on replicating the training process. # Sponsors

Speechcatcher was gracefully funded by

<a href="URL">Media Tech Lab by Media Lab Bayern</a> (<a href="URL

<a href="URL">
    <img src="URL width="240" title="Media Tech Lab powered by logo">
</a> # Citing # ASR config

<details><summary>expand</summary>



</details>
TAGS
#espnet #audio #automatic-speech-recognition #de #dataset-speechcatcher #license-mit #region-us 
## Speechcatcher ESPnet streaming ASR model XL for German ASR### 'speechcatcher/speechcatcher_german_espnet_streaming_transformer_26k_train_size_xl_raw_de_bpe1024'

This model was trained by bmilde using the Speechcatcher recipe in espnet.### Demo: How to use the model

Global installation:



Virtual environment:# RESULTS

Tuda-de-raw: 2.4% CER (without LM)

Tuda-de-raw: 8.5% WER (without LM)

Note: Tuda-de-raw results are based on raw tuda-de test utterances without the normalization step. It may not be directly comparable to regular tuda-de results.# Speechcatcher training

Speechcatcher models are trained by using Whisper large as a teacher model:

!Speechcatcher Teacher/student training

See speechcatcher-data for code and more info on replicating the training process.# Sponsors

Speechcatcher was gracefully funded by

<a href="URL">Media Tech Lab by Media Lab Bayern</a> (<a href="URL

<a href="URL">
    <img src="URL width="240" title="Media Tech Lab powered by logo">
</a># Citing# ASR config

<details><summary>expand</summary>



</details>