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20
README.md
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@ -55,16 +55,16 @@ FunASR has open-sourced a large number of pre-trained models on industrial data.
(Note: 🤗 represents the Huggingface model zoo link, ⭐ represents the ModelScope model zoo link) (Note: 🤗 represents the Huggingface model zoo link, ⭐ represents the ModelScope model zoo link)
| Model Name | Task Details | Training Data | Parameters | | Model Name | Task Details | Training Data | Parameters |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------:|:--------------------------------:|:----------:| |:------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------:|:--------------------------------:|:----------:|
| paraformer-zh <br> ([⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) [🤗]() ) | speech recognition, with timestamps, non-streaming | 60000 hours, Mandarin | 220M | | paraformer-zh <br> ([⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) [🤗]() ) | speech recognition, with timestamps, non-streaming | 60000 hours, Mandarin | 220M |
| paraformer-zh-spk <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary) [🤗]() ) | speech recognition with speaker diarization, with timestamps, non-streaming | 60000 hours, Mandarin | 220M | | <nobr>paraformer-zh-online <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗]() )</nobr> | speech recognition, streaming | 60000 hours, Mandarin | 220M |
| <nobr>paraformer-zh-online <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗]() )</nobr> | speech recognition, streaming | 60000 hours, Mandarin | 220M | | paraformer-en <br> ( [⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) [🤗]() ) | speech recognition, with timestamps, non-streaming | 50000 hours, English | 220M |
| paraformer-en <br> ( [⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) [🤗]() ) | speech recognition, with timestamps, non-streaming | 50000 hours, English | 220M | | conformer-en <br> ( [⭐](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) [🤗]() ) | speech recognition, non-streaming | 50000 hours, English | 220M |
| conformer-en <br> ( [⭐](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) [🤗]() ) | speech recognition, non-streaming | 50000 hours, English | 220M | | ct-punc <br> ( [⭐](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) [🤗]() ) | punctuation restoration | 100M, Mandarin and English | 1.1G |
| ct-punc <br> ( [⭐](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) [🤗]() ) | punctuation restoration | 100M, Mandarin and English | 1.1G | | fsmn-vad <br> ( [⭐](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) [🤗]() ) | voice activity detection | 5000 hours, Mandarin and English | 0.4M |
| fsmn-vad <br> ( [⭐](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) [🤗]() ) | voice activity detection | 5000 hours, Mandarin and English | 0.4M | | fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗]() ) | timestamp prediction | 5000 hours, Mandarin | 38M |
| fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗]() ) | timestamp prediction | 5000 hours, Mandarin | 38M | | cam++ <br> ( [⭐](https://modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) [🤗]() ) | speaker verification/diarization | 5000 hours | 7.2M |

16
README_zh.md
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@ -57,16 +57,16 @@ FunASR开源了大量在工业数据上预训练模型，您可以在[模型许
（注：[🤗]()表示Huggingface模型仓库链接，[⭐]()表示ModelScope模型仓库链接） （注：[🤗]()表示Huggingface模型仓库链接，[⭐]()表示ModelScope模型仓库链接）
| 模型名字 | 任务详情 | 训练数据 | 参数量 | | 模型名字 | 任务详情 | 训练数据 | 参数量 |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------:|:------------:|:----:| |:------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------:|:------------:|:----:|
| paraformer-zh <br> ([⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) [🤗]() ) | 语音识别，带时间戳输出，非实时 | 60000小时，中文 | 220M | | paraformer-zh <br> ([⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) [🤗]() ) | 语音识别，带时间戳输出，非实时 | 60000小时，中文 | 220M |
| paraformer-zh-spk <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary) [🤗]() ) | 分角色语音识别，带时间戳输出，非实时 | 60000小时，中文 | 220M | | paraformer-zh-streaming <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗]() ) | 语音识别，实时 | 60000小时，中文 | 220M |
| paraformer-zh-streaming <br> ( [⭐](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) [🤗]() ) | 语音识别，实时 | 60000小时，中文 | 220M | | paraformer-en <br> ( [⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) [🤗]() ) | 语音识别，非实时 | 50000小时，英文 | 220M |
| paraformer-en <br> ( [⭐](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/summary) [🤗]() ) | 语音识别，非实时 | 50000小时，英文 | 220M | | conformer-en <br> ( [⭐](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) [🤗]() ) | 语音识别，非实时 | 50000小时，英文 | 220M |
| conformer-en <br> ( [⭐](https://modelscope.cn/models/damo/speech_conformer_asr-en-16k-vocab4199-pytorch/summary) [🤗]() ) | 语音识别，非实时 | 50000小时，英文 | 220M | | ct-punc <br> ( [⭐](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) [🤗]() ) | 标点恢复 | 100M，中文与英文 | 1.1G |
| ct-punc <br> ( [⭐](https://modelscope.cn/models/damo/punc_ct-transformer_cn-en-common-vocab471067-large/summary) [🤗]() ) | 标点恢复 | 100M，中文与英文 | 1.1G | | fsmn-vad <br> ( [⭐](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) [🤗]() ) | 语音端点检测，实时 | 5000小时，中文与英文 | 0.4M |
| fsmn-vad <br> ( [⭐](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) [🤗]() ) | 语音端点检测，实时 | 5000小时，中文与英文 | 0.4M | | fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗]() ) | 字级别时间戳预测 | 50000小时，中文 | 38M |
| fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗]() ) | 字级别时间戳预测 | 50000小时，中文 | 38M | | cam++ <br> ( [⭐](https://modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) [🤗]() ) | 说话人确认/分割 | 5000小时 | 7.2M |
<a name="快速开始"></a> <a name="快速开始"></a>

6
examples/industrial_data_pretraining/uniasr/demo.py
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@ -5,11 +5,7 @@
from funasr import AutoModel from funasr import AutoModel
model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online", model_revision="v2.0.4", model = AutoModel(model="iic/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline", model_revision="v2.0.4",
# vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
# vad_model_revision="v2.0.4",
# punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
# punc_model_revision="v2.0.4",
) )
res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav") res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")

3
funasr/auto/auto_model.py
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@ -224,7 +224,7 @@ class AutoModel:
asr_result_list = [] asr_result_list = []
num_samples = len(data_list) num_samples = len(data_list)
disable_pbar = kwargs.get("disable_pbar", False) disable_pbar = kwargs.get("disable_pbar", False)
pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True) if not disable_pbar else None pbar = tqdm(colour="blue", total=num_samples, dynamic_ncols=True) if not disable_pbar else None
time_speech_total = 0.0 time_speech_total = 0.0
time_escape_total = 0.0 time_escape_total = 0.0
for beg_idx in range(0, num_samples, batch_size): for beg_idx in range(0, num_samples, batch_size):
@ -350,6 +350,7 @@ class AutoModel:
end_asr_total = time.time() end_asr_total = time.time()
time_escape_total_per_sample = end_asr_total - beg_asr_total time_escape_total_per_sample = end_asr_total - beg_asr_total
pbar_sample.update(1)
pbar_sample.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, " pbar_sample.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, " f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}") f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")

52
funasr/models/uniasr/template.yaml
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@ -18,6 +18,7 @@ model_conf:
decoder_attention_chunk_type2: chunk decoder_attention_chunk_type2: chunk
loss_weight_model1: 0.5 loss_weight_model1: 0.5
# encoder # encoder
encoder: SANMEncoderChunkOpt encoder: SANMEncoderChunkOpt
encoder_conf: encoder_conf:
@ -34,11 +35,21 @@ encoder_conf:
kernel_size: 11 kernel_size: 11
sanm_shfit: 0 sanm_shfit: 0
selfattention_layer_type: sanm selfattention_layer_type: sanm
chunk_size: [20, 60] chunk_size:
stride: [10, 40] - 20
pad_left: [5, 10] - 60
encoder_att_look_back_factor: [0, 0] stride:
decoder_att_look_back_factor: [0, 0] - 10
- 40
pad_left:
- 5
- 10
encoder_att_look_back_factor:
- 0
- 0
decoder_att_look_back_factor:
- 0
- 0
# decoder # decoder
decoder: FsmnDecoderSCAMAOpt decoder: FsmnDecoderSCAMAOpt
@ -55,6 +66,7 @@ decoder_conf:
kernel_size: 11 kernel_size: 11
concat_embeds: true concat_embeds: true
# predictor
predictor: CifPredictorV2 predictor: CifPredictorV2
predictor_conf: predictor_conf:
idim: 320 idim: 320
@ -62,6 +74,8 @@ predictor_conf:
l_order: 1 l_order: 1
r_order: 1 r_order: 1
# encoder2
encoder2: SANMEncoderChunkOpt encoder2: SANMEncoderChunkOpt
encoder2_conf: encoder2_conf:
output_size: 320 output_size: 320
@ -77,12 +91,23 @@ encoder2_conf:
kernel_size: 21 kernel_size: 21
sanm_shfit: 0 sanm_shfit: 0
selfattention_layer_type: sanm selfattention_layer_type: sanm
chunk_size: [45, 70] chunk_size:
stride: [35, 50] - 45
pad_left: [5, 10] - 70
encoder_att_look_back_factor: [0, 0] stride:
decoder_att_look_back_factor: [0, 0] - 35
- 50
pad_left:
- 5
- 10
encoder_att_look_back_factor:
- 0
- 0
decoder_att_look_back_factor:
- 0
- 0
# decoder
decoder2: FsmnDecoderSCAMAOpt decoder2: FsmnDecoderSCAMAOpt
decoder2_conf: decoder2_conf:
attention_dim: 320 attention_dim: 320
@ -108,10 +133,12 @@ stride_conv: stride_conv1d
stride_conv_conf: stride_conv_conf:
kernel_size: 2 kernel_size: 2
stride: 2 stride: 2
pad: [0, 1] pad:
- 0
- 1
# frontend related # frontend related
frontend: WavFrontendOnline frontend: WavFrontend
frontend_conf: frontend_conf:
fs: 16000 fs: 16000
window: hamming window: hamming
@ -120,6 +147,7 @@ frontend_conf:
frame_shift: 10 frame_shift: 10
lfr_m: 7 lfr_m: 7
lfr_n: 6 lfr_n: 6
dither: 0.0
specaug: SpecAugLFR specaug: SpecAugLFR
specaug_conf: specaug_conf:

42
model_zoo/modelscope_models_zh.md
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@ -33,26 +33,26 @@
#### UniASR模型 #### UniASR模型
| 模型名字 | 语言 | 训练数据 | Vocab Size | Parameter | 非实时/实时 | 备注 | | 模型名字 | 语言 | 训练数据 | Vocab Size | Parameter | 非实时/实时 | 备注 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------| |:---------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|:---------------------------------:|:----------:|:---------:|:--------------:|:--------------------------------------------------------------------------------------------------------------------------------|
| [UniASR](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-实时/summary) | 中文和英文 | 阿里巴巴语音数据 (60000 小时) | 8358 | 100M | 实时 | 流式离线一体化模型 | | [UniASR](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-online/summary) | 中文和英文 | 阿里巴巴语音数据 (60000 小时) | 8358 | 100M | 实时 | 流式离线一体化模型 |
| [UniASR-large](https://modelscope.cn/models/damo/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-非实时/summary) | 中文和英文 | 阿里巴巴语音数据 (60000 小时) | 8358 | 220M | 非实时 | 流式离线一体化模型 | | [UniASR-large](https://modelscope.cn/models/damo/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline/summary) | 中文和英文 | 阿里巴巴语音数据 (60000 小时) | 8358 | 220M | 非实时 | 流式离线一体化模型 |
| [UniASR English](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-实时/summary) | 英文 | 阿里巴巴语音数据 (10000 小时) | 1080 | 95M | 实时 | 流式离线一体化模型 | | [UniASR English](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-online/summary) | 英文 | 阿里巴巴语音数据 (10000 小时) | 1080 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Russian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-实时/summary) | 俄语 | 阿里巴巴语音数据 (5000 小时) | 1664 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Russian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-online/summary) | 俄语 | 阿里巴巴语音数据 (5000 小时) | 1664 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Japanese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-实时/summary) | 日语 | 阿里巴巴语音数据 (5000 小时) | 5977 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Japanese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-online/summary) | 日语 | 阿里巴巴语音数据 (5000 小时) | 5977 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Korean](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-实时/summary) | 韩语 | 阿里巴巴语音数据 (2000 小时) | 6400 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Korean](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-online/summary) | 韩语 | 阿里巴巴语音数据 (2000 小时) | 6400 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Cantonese (CHS)](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-实时/summary) | 粤语（简体中文） | 阿里巴巴语音数据 (5000 小时) | 1468 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Cantonese (CHS)](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-online/summary) | 粤语（简体中文） | 阿里巴巴语音数据 (5000 小时) | 1468 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Indonesian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-实时/summary) | 印尼语 | 阿里巴巴语音数据 (1000 小时) | 1067 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Indonesian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-id-16k-common-vocab1067-tensorflow1-online/summary) | 印尼语 | 阿里巴巴语音数据 (1000 小时) | 1067 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Vietnamese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-实时/summary) | 越南语 | 阿里巴巴语音数据 (1000 小时) | 1001 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Vietnamese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online/summary) | 越南语 | 阿里巴巴语音数据 (1000 小时) | 1001 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Spanish](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-实时/summary) | 西班牙语 | 阿里巴巴语音数据 (1000 小时) | 3445 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Spanish](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-online/summary) | 西班牙语 | 阿里巴巴语音数据 (1000 小时) | 3445 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Portuguese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-实时/summary) | 葡萄牙语 | 阿里巴巴语音数据 (1000 小时) | 1617 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Portuguese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-pt-16k-common-vocab1617-tensorflow1-online/summary) | 葡萄牙语 | 阿里巴巴语音数据 (1000 小时) | 1617 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR French](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-实时/summary) | 法语 | 阿里巴巴语音数据 (1000 小时) | 3472 | 95M | 实时 | 流式离线一体化模型 | | [UniASR French](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online/summary) | 法语 | 阿里巴巴语音数据 (1000 小时) | 3472 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR German](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-实时/summary) | 德语 | 阿里巴巴语音数据 (1000 小时) | 3690 | 95M | 实时 | 流式离线一体化模型 | | [UniASR German](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online/summary) | 德语 | 阿里巴巴语音数据 (1000 小时) | 3690 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Persian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-实时/summary) | 波斯语 | 阿里巴巴语音数据 (1000 小时) | 1257 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Persian](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/summary) | 波斯语 | 阿里巴巴语音数据 (1000 小时) | 1257 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Burmese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-my-16k-common-vocab696-pytorch/summary) | 缅甸语 | 阿里巴巴语音数据 (1000 小时) | 696 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Burmese](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-my-16k-common-vocab696-pytorch/summary) | 缅甸语 | 阿里巴巴语音数据 (1000 小时) | 696 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Hebrew](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-he-16k-common-vocab1085-pytorch/summary) | 希伯来语 | 阿里巴巴语音数据 (1000 小时) | 1085 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Hebrew](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-he-16k-common-vocab1085-pytorch/summary) | 希伯来语 | 阿里巴巴语音数据 (1000 小时) | 1085 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Urdu](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ur-16k-common-vocab877-pytorch/summary) | 乌尔都语 | 阿里巴巴语音数据 (1000 小时) | 877 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Urdu](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-ur-16k-common-vocab877-pytorch/summary) | 乌尔都语 | 阿里巴巴语音数据 (1000 小时) | 877 | 95M | 实时 | 流式离线一体化模型 |
| [UniASR Turkish](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/summary) | 土耳其语 | 阿里巴巴语音数据 (1000 小时) | 1582 | 95M | 实时 | 流式离线一体化模型 | | [UniASR Turkish](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/summary) | 土耳其语 | 阿里巴巴语音数据 (1000 小时) | 1582 | 95M | 实时 | 流式离线一体化模型 |
#### Conformer模型 #### Conformer模型
@ -115,7 +115,7 @@
| 模型名字 | 语言 | 训练数据 | 模型参数 | 备注 | | 模型名字 | 语言 | 训练数据 | 模型参数 | 备注 |
|:--------------------------------------------------------------------------------------------------:|:--------------:|:-------------------:|:----------:|:---------| |:--------------------------------------------------------------------------------------------------:|:--------------:|:-------------------:|:----------:|:---------|
| [TP-Aligner](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-非实时/summary) |中文| 阿里巴巴语音数据 (50000hours) | 37.8M | 时间戳模型，中文 | | [TP-Aligner](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) |中文| 阿里巴巴语音数据 (50000hours) | 37.8M | 时间戳模型，中文 |
### 逆文本正则化 ### 逆文本正则化