funasr1.0.2

README.md

@@ -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)
 
 
-|                                                                             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-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    |
-|         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    |
-|                     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    | 
-|                          fa-zh <br> ( [⭐](https://modelscope.cn/models/damo/speech_timestamp_prediction-v1-16k-offline/summary) [🤗]() )                           |                            timestamp prediction                             |       5000 hours, Mandarin       |    38M     | 
+|                                                                             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    |
+| <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    |
+|                     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    | 
+|                          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     | 
+|                cam++ <br> ( [⭐](https://modelscope.cn/models/iic/speech_campplus_sv_zh-cn_16k-common/summary) [🤗]() )                                             |        speaker verification/diarization            |            5000 hours            |    7.2M    | 
 
 
 

README_zh.md

@@ -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）
 
 
-|                                                                             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-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-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 |
-| 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 | 
-| 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  |
+|   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 |
+|                  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 | 
+|                       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  |
+|                           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>

examples/industrial_data_pretraining/uniasr/demo.py

@@ -5,11 +5,7 @@
 
 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",
-                  # 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",
+model = AutoModel(model="iic/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline", 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")

funasr/auto/auto_model.py

@@ -224,7 +224,7 @@ class AutoModel:
         asr_result_list = []
         num_samples = len(data_list)
         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_escape_total = 0.0
         for beg_idx in range(0, num_samples, batch_size):
@@ -350,6 +350,7 @@ class AutoModel:
             
             end_asr_total = time.time()
             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}, "
                                  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}")

funasr/models/uniasr/template.yaml

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

model_zoo/modelscope_models_zh.md

@@ -33,26 +33,26 @@
 
 #### UniASR模型
 
-|                                                                    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-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 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 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 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 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 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 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 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 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 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 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 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 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 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 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                                                                                                    |
+|                                                                     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-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-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-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-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-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-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-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-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-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-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-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-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-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-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 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 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
@@ -115,7 +115,7 @@
 
 |                                                    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