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编写于
12月 13, 2021
作者:
H
Hui Zhang
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12月 13, 2021
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fix release model (#1106)
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@@ -2,32 +2,31 @@
## Speech-to-Text Models
###
Acoustic Model Released in paddle 2.X
Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech |
example link
###
Speech Recognition Model
Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech |
Example Link
:-------------:| :------------:| :-----: | -----: | :----------------- |:--------- | :---------- | :--------- | :-----------
[
Ds2 Online Aishell ASR0 Model
](
https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/aishell_ds2_online_cer8.00_release.tar.gz
)
| Aishell Dataset | Char-based | 345 MB | 2 Conv + 5 LSTM layers with only forward direction | 0.080 |-| 151 h |
[
D2 Online Aishell S0 Example
](
../../examples/aishell/asr0
)
[
Ds2 Offline Aishell ASR0 Model
](
https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz
)
| Aishell Dataset | Char-based | 306 MB | 2 Conv + 3 bidirectional GRU layers| 0.064 |-| 151 h |
[
Ds2 Offline Aishell S0 Example
](
../../examples/aishell/asr0
)
[
Conformer Online Aishell ASR1 Model
](
https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.chunk.release.tar.gz
)
| Aishell Dataset | Char-based | 283 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0594 |-| 151 h |
[
Conformer Online Aishell S1 Example
](
../../examples/aishell/s1
)
[
Conformer Offline Aishell ASR1 Model
](
https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz
)
| Aishell Dataset | Char-based | 284 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0547 |-| 151 h |
[
Conformer Offline Aishell S1 Example
](
../../examples/aishell/s1
)
[
Conformer Librispeech ASR1 Model
](
https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz
)
| Librispeech Dataset | subword-based | 287 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0325 | 960 h |
[
Conformer Librispeech S1 example
](
../../example/librispeech/s1
)
[
Transformer Librispeech ASR1 Model
](
https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/transformer.model.tar.gz
)
| Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0410 | 960 h |
[
Transformer Librispeech S1 example
](
../../example/librispeech/s1
)
[
Transformer Librispeech ASR2 Model
](
https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/transformer.model.tar.gz
)
| Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: JoinCTC w/ LM |-| 0.024 | 960 h |
[
Transformer Librispeech S2 example
](
../../example/librispeech/s2
)
### Acoustic Model Transformed from paddle 1.8
Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech
:-------------:| :------------:| :-----: | -----: | :----------------- | :---------- | :---------- | :---------
[
Ds2 Offline Aishell model
](
https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_v1.8_to_v2.x.tar.gz
)
|Aishell Dataset| Char-based| 234 MB| 2 Conv + 3 bidirectional GRU layers| 0.0804 |-| 151 h|
[
Ds2 Offline Librispeech model
](
https://deepspeech.bj.bcebos.com/eng_models/librispeech_v1.8_to_v2.x.tar.gz
)
|Librispeech Dataset| Word-based| 307 MB| 2 Conv + 3 bidirectional sharing weight RNN layers |-| 0.0685| 960 h|
[
Ds2 Offline Baidu en8k model
](
https://deepspeech.bj.bcebos.com/eng_models/baidu_en8k_v1.8_to_v2.x.tar.gz
)
|Baidu Internal English Dataset| Word-based| 273 MB| 2 Conv + 3 bidirectional GRU layers |-| 0.0541 | 8628 h|
### Language Model Released
[
Ds2 Online Aishell ASR0 Model
](
https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/aishell_ds2_online_cer8.00_release.tar.gz
)
| Aishell Dataset | Char-based | 345 MB | 2 Conv + 5 LSTM layers with only forward direction | 0.080 |-| 151 h |
[
D2 Online Aishell ASR0
](
../../examples/aishell/asr0
)
[
Ds2 Offline Aishell ASR0 Model
](
https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/ds2.model.tar.gz
)
| Aishell Dataset | Char-based | 306 MB | 2 Conv + 3 bidirectional GRU layers| 0.064 |-| 151 h |
[
Ds2 Offline Aishell ASR0
](
../../examples/aishell/asr0
)
[
Conformer Online Aishell ASR1 Model
](
https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.chunk.release.tar.gz
)
| Aishell Dataset | Char-based | 283 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0594 |-| 151 h |
[
Conformer Online Aishell ASR1
](
../../examples/aishell/asr1
)
[
Conformer Offline Aishell ASR1 Model
](
https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz
)
| Aishell Dataset | Char-based | 284 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0547 |-| 151 h |
[
Conformer Offline Aishell ASR1
](
../../examples/aishell/asr1
)
[
Transformer Aishell ASR1 Model
](
https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/transformer.model.tar.gz
)
| Aishell Dataset | Char-based | 128 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0538 || 151 h |
[
Transformer Aishell ASR1
](
../../examples/aishell/asr1
)
[
Conformer Librispeech ASR1 Model
](
https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/conformer.model.tar.gz
)
| Librispeech Dataset | subword-based | 191 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0337 | 960 h |
[
Conformer Librispeech ASR1
](
../../example/librispeech/asr1
)
[
Transformer Librispeech ASR1 Model
](
https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/transformer.model.tar.gz
)
| Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0381 | 960 h |
[
Transformer Librispeech ASR1
](
../../example/librispeech/asr1
)
[
Transformer Librispeech ASR2 Model
](
https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr2/transformer.model.tar.gz
)
| Librispeech Dataset | subword-based | 131 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: JoinCTC w/ LM |-| 0.0240 | 960 h |
[
Transformer Librispeech ASR2
](
../../example/librispeech/asr2
)
### Language Model based on NGram
Language Model | Training Data | Token-based | Size | Descriptions
:-------------:| :------------:| :-----: | -----: | :-----------------
[
English LM
](
https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm
)
|
[
CommonCrawl(en.00)
](
http://web-language-models.s3-website-us-east-1.amazonaws.com/ngrams/en/deduped/en.00.deduped.xz
)
| Word-based | 8.3 GB | Pruned with 0 1 1 1 1;
<br/>
About 1.85 billion n-grams;
<br/>
'trie' binary with '-a 22 -q 8 -b 8'
[
Mandarin LM Small
](
https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
)
| Baidu Internal Corpus | Char-based | 2.8 GB | Pruned with 0 1 2 4 4;
<br/>
About 0.13 billion n-grams;
<br/>
'probing' binary with default settings
[
Mandarin LM Large
](
https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm
)
| Baidu Internal Corpus | Char-based | 70.4 GB | No Pruning;
<br/>
About 3.7 billion n-grams;
<br/>
'probing' binary with default settings
### Speech Translation Models
| Model | Training Data | Token-based | Size | Descriptions | BLEU | Example Link |
| ------------------------------------------------------------ | ------------- | ----------- | ---- | ------------------------------------------------------------ | ----- | ------------------------------------------------------------ |
|
[
Transformer FAT-ST MTL En-Zh
](
https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_ted-en-zh.tar.gz
)
| Ted-En-Zh | Spm | | Encoder:Transformer, Decoder:Transformer,
<br
/>
Decoding method: Attention | 20.80 |
[
Transformer Ted-En-Zh ST1
](
https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/examples/ted_en_zh/st1
)
|
## Text-to-Speech Models
...
...
@@ -69,8 +68,11 @@ PANN | Audioset| [audioset_tagging_cnn](https://github.com/qiuqiangkong/audioset
PANN | ESC-50 |
[
pann-esc50
](
"./examples/esc50/cls0"
)
|
[
panns_cnn6.tar.gz
](
https://paddlespeech.bj.bcebos.com/cls/panns_cnn6.tar.gz
)
,
[
panns_cnn10
](
https://paddlespeech.bj.bcebos.com/cls/panns_cnn10.tar.gz
)
,
[
panns_cnn14.tar.gz
](
https://paddlespeech.bj.bcebos.com/cls/panns_cnn14.tar.gz
)
## Speech Translation Models
## Speech Recognition Model from paddle 1.8
| Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER | Hours of speech |
| :----------------------------------------------------------: | :----------------------------: | :---------: | -----: | :------------------------------------------------- | :----- | :----- | :-------------- |
|
[
Ds2 Offline Aishell model
](
https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_v1.8_to_v2.x.tar.gz
)
| Aishell Dataset | Char-based | 234 MB | 2 Conv + 3 bidirectional GRU layers | 0.0804 | - | 151 h |
|
[
Ds2 Offline Librispeech model
](
https://deepspeech.bj.bcebos.com/eng_models/librispeech_v1.8_to_v2.x.tar.gz
)
| Librispeech Dataset | Word-based | 307 MB | 2 Conv + 3 bidirectional sharing weight RNN layers | - | 0.0685 | 960 h |
|
[
Ds2 Offline Baidu en8k model
](
https://deepspeech.bj.bcebos.com/eng_models/baidu_en8k_v1.8_to_v2.x.tar.gz
)
| Baidu Internal English Dataset | Word-based | 273 MB | 2 Conv + 3 bidirectional GRU layers | - | 0.0541 | 8628 h |
Model Type | Dataset| Example Link | Pretrained Models | Model Size
:-------------:| :------------:| :-----: | :-----: | :-----:
FAT-ST | TED En-Zh |
[
FAT + Transformer+ASR MTL
](
./examples/ted_en_zh/st1
)
|
[
fat_st_ted-en-zh.tar.gz
](
https://paddlespeech.bj.bcebos.com/s2t/ted_en_zh/st1/fat_st_ted-en-zh.tar.gz
)
| 50.26M
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