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LICENSE

@@ -0,0 +1,140 @@
+The MooER from Moore Threads is licensed under the MIT License listed below. Copyright (c) 2023-2024 Moore Threads Technology Co., Ltd("Moore Threads"). All rights reserved.
+Terms of the MIT License
+-------------------------------------------------------------------------
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+-------------------------------------------------------------------------
+The following copyright statements and licenses apply to various open source software/model
+packages (or portions thereof) that are distributed with this MooER. MooER that
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+-------------------------------------------------------------------------
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+-------------------------------------------------------------------------
+FunASR
+
+MIT License
+
+Copyright (c) 2022 Alibaba
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
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+Copyright (C) [2023-2028] Alibaba Group. All rights reserved.
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+Thank you for choosing the FunASR open source models. The FunASR open source models contain a series of open-source models that allow everyone to use, modify, share, and learn from it.
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+To ensure better community collaboration, we have developed the following agreement and hope that you carefully read and abide by it.
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+In this agreement, [FunASR software] refers to the FunASR open source model, and its derivatives, including fine-tuned models. [You] refer to individuals or organizations who use, modify, share, and learn from [FunASR software].
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+If you violate any terms of this agreement, your license will be automatically terminated, and you must stop using, copying, modifying, and sharing [FunASR software].
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+FunASR 模型开源协议
+
+版本号：1.0
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+版权所有 (C) [2023-2028] [阿里巴巴集团]。保留所有权利。
+
+感谢您选择 FunASR 开源模型。FunASR 开源模型包含一系列免费且开源的工业模型，让大家可以使用、修改、分享和学习该模型。
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+
+1 定义
+本协议中，[FunASR 软件]指 FunASR 开源模型权重及其衍生品，包括 Finetune 后的模型；[您]指使用、修改、分享和学习[FunASR 软件]的个人或组织。
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+2 许可和限制
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+您可以在遵守本协议的前提下，自由地使用、复制、修改和分享[FunASR 软件]。
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+本协议可能会不时更新和修订。修订后的协议将在[FunASR 软件]官方仓库发布，并自动生效。如果您继续使用、复制、修改和分享[FunASR 软件]，即表示您同意修订后的协议。
+
+6 其他规定
+本协议受到[国家/地区] 的法律管辖。如果任何条款被裁定为不合法、无效或无法执行，则该条款应被视为从本协议中删除，而其余条款应继续有效并具有约束力。
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+
+版权所有© [2023-2028] [阿里巴巴集团]。保留所有权利。
+-------------------------------------------------------------------------
+Qwen/Qwen2-7B-Instruct
+Copyright [yyyy] [name of copyright owner]
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
+

Qwen2-7B-Instruct/README.md

@@ -0,0 +1,11 @@
+Please download Qwen2-7B-Instruct manually.
+
+Qwen2-7B-Instruct can be downloaded from HuggingFace or ModelScope by:
+
+```
+# from huggingface
+git clone https://huggingface.co/Qwen/Qwen2-7B-Instruct
+
+# from modelscope
+git clone https://modelscope.cn/models/qwen/Qwen2-7B-Instruct
+```

README.md

@@ -1,3 +1,280 @@
 ---
 license: mit
+language:
+- zh
+- en
+metrics:
+- cer
+- bleu
+tags:
+- asr
+- automatic-speech-recognition
+- automatic-speech-translation
+- speech-translation
+- speech-recognition
 ---
+
+
+# MooER (摩耳): an LLM-based Speech Recognition and Translation Model from Moore Threads
+
+**Online Demo**: https://mooer-speech.mthreads.com:10077/
+
+## 🔥 Update
+
+We release a new model *MooER-80K-v2* using 80K hours of data. Currently, *MooER-80K-v2* supports the ASR task. The AST and multi-task models will be released soon.
+
+## 📖 Introduction
+
+We introduce **MooER (摩耳)**: an LLM-based speech recognition and translation model developed by Moore Threads. With the *MooER* framework, you can transcribe the speech into text (speech recognition or, ASR), and  translate it into other languages (speech translation or, AST) in a end-to-end manner. The performance of *MooER* is demonstrated in the subsequent section, along with our insights into model configurations, training strategies, and more, provided in our [technical report](https://arxiv.org/abs/2408.05101).
+
+For the usage of the model files, please refer to our [GitHub](https://github.com/MooreThreads/MooER)
+
+<br>
+<p align="center">
+    <img src="assets/framework.png" width="600"/>
+<p>
+<br>
+
+## 🥊 Evaluation Results
+
+We demonstrate the training data and the evaluation results below. For more comprehensive information, please refer to our [report](https://arxiv.org/pdf/2408.05101).
+
+### Training data
+
+We utilize 5k hours of data (MT5K) to train our basic *MooER-5K* model. The data sources include:
+
+| Dataset          | Duration          |
+|---------------|---------------|
+| aishell2 | 137h          |
+| librispeech | 131h      |
+| multi_cn | 100h          |
+| wenetspeech  | 1361h     |
+| in-house data | 3274h  |
+
+Note that, data from the open-source datasets were randomly selected from the full training set. The in-house data, collected internally without text, were transcribed using a third-party ASR service.
+
+Since all the above datasets were originally designed only for the speech recognition task, no translation results are available. To train our speech translation model, we used a third-party translation service to generate pseudo-labels. No data filtering techniques were applied.
+
+At this moment, we are also developing a new model trained with 80K hours of data.
+
+### Speech Recognition
+
+The performance of speech recognition is evaluated using WER/CER.
+
+<table>
+  <tr>
+    <th>Language</th>
+    <th>Testset</th>
+    <th>Paraformer-large</th>
+    <th>SenseVoice-small</th>
+    <th>Qwen-audio</th>
+    <th>Whisper-large-v3</th>
+    <th>SeamlessM4T-v2</th>
+    <th>MooER-5K</th>
+    <th>MooER-80K</th>
+    <th>MooER-80K-v2</th>
+  </tr>
+  <tr>
+    <td rowspan="7">Chinese</td>
+    <td>aishell1</td>
+    <td>1.93</td>
+    <td>3.03</td>
+    <td>1.43</td>
+    <td>7.86</td>
+    <td>4.09</td>
+    <td>1.93</td>
+    <td>1.25</td>
+    <td>1.00</td>
+  </tr>
+  <tr>
+    <td>aishell2_ios</td>
+    <td>2.85</td>
+    <td>3.79</td>
+    <td>3.57</td>
+    <td>5.38</td>
+    <td>4.81</td>
+    <td>3.17</td>
+    <td>2.67</td>
+    <td>2.62</td>
+  </tr>
+  <tr>
+    <td>test_magicdata</td>
+    <td>3.66</td>
+    <td>3.81</td>
+    <td>5.31</td>
+    <td>8.36</td>
+    <td>9.69</td>
+    <td>3.48</td>
+    <td>2.52</td>
+    <td>2.17</td>
+  </tr>
+  <tr>
+    <td>test_thchs</td>
+    <td>3.99</td>
+    <td>5.17</td>
+    <td>4.86</td>
+    <td>9.06</td>
+    <td>7.14</td>
+    <td>4.11</td>
+    <td>3.14</td>
+    <td>3.00</td>
+  </tr>
+  <tr>
+    <td>fleurs cmn_dev</td>
+    <td>5.56</td>
+    <td>6.39</td>
+    <td>10.54</td>
+    <td>4.54</td>
+    <td>7.12</td>
+    <td>5.81</td>
+    <td>5.23</td>
+    <td>5.15</td>
+  </tr>
+  <tr>
+    <td>fleurs cmn_test</td>
+    <td>6.92</td>
+    <td>7.36</td>
+    <td>11.07</td>
+    <td>5.24</td>
+    <td>7.66</td>
+    <td>6.77</td>
+    <td>6.18</td>
+    <td>6.14</td>
+  </tr>
+  <tr>
+    <td>average</td>
+    <td><strong>4.15</strong></td>
+    <td><strong>4.93</strong></td>
+    <td><strong>6.13</strong></td>
+    <td><strong>6.74</strong></td>
+    <td><strong>6.75</strong></td>
+    <td><strong>4.21</strong></td>
+    <td><strong>3.50</strong></td>
+    <td><strong>3.35</strong></td>
+  </tr>
+  <tr>
+    <td rowspan="7">English</td>
+    <td>librispeech test_clean</td>
+    <td>14.15</td>
+    <td>4.07</td>
+    <td>2.15</td>
+    <td>3.42</td>
+    <td>2.77</td>
+    <td>7.78</td>
+    <td>4.11</td>
+    <td>3.57</td>
+  </tr>
+  <tr>
+    <td>librispeech test_other</td>
+    <td>22.99</td>
+    <td>8.26</td>
+    <td>4.68</td>
+    <td>5.62</td>
+    <td>5.25</td>
+    <td>15.25</td>
+    <td>9.99</td>
+    <td>9.09</td>
+  </tr>
+  <tr>
+    <td>fleurs eng_dev</td>
+    <td>24.93</td>
+    <td>12.92</td>
+    <td>22.53</td>
+    <td>11.63</td>
+    <td>11.36</td>
+    <td>18.89</td>
+    <td>13.32</td>
+    <td>13.12</td>
+  </tr>
+  <tr>
+    <td>fleurs eng_test</td>
+    <td>26.81</td>
+    <td>13.41</td>
+    <td>22.51</td>
+    <td>12.57</td>
+    <td>11.82</td>
+    <td>20.41</td>
+    <td>14.97</td>
+    <td>14.74</td>
+  </tr>
+  <tr>
+    <td>gigaspeech dev</td>
+    <td>24.23</td>
+    <td>19.44</td>
+    <td>12.96</td>
+    <td>19.18</td>
+    <td>28.01</td>
+    <td>23.46</td>
+    <td>16.92</td>
+    <td>17.34</td>
+  </tr>
+  <tr>
+    <td>gigaspeech test</td>
+    <td>23.07</td>
+    <td>16.65</td>
+    <td>13.26</td>
+    <td>22.34</td>
+    <td>28.65</td>
+    <td>22.09</td>
+    <td>16.64</td>
+    <td>16.97</td>
+  </tr>
+  <tr>
+    <td>average</td>
+    <td><strong>22.70</strong></td>
+    <td><strong>12.46</strong></td>
+    <td><strong>13.02</strong></td>
+    <td><strong>12.46</strong></td>
+    <td><strong>14.64</strong></td>
+    <td><strong>17.98</strong></td>
+    <td><strong>12.66</strong></td>
+    <td><strong>12.47</strong></td>
+  </tr>
+</table>
+
+### Speech Translation (zh -> en)
+
+For speech translation, the performanced is evaluated using BLEU score.
+
+| Testset | Speech-LLaMA | Whisper-large-v3 | Qwen-audio | Qwen2-audio | SeamlessM4T-v2 | MooER-5K | MooER-5K-MTL |
+|--------|-------------|-------------------|------------|-------------|-----------------|--------|--------------|
+|CoVoST1 zh2en | - |  13.5 | 13.5 | - | 25.3 | - | **30.2** |
+|CoVoST2 zh2en | 12.3 | 12.2 | 15.7 | 24.4 | 22.2 | 23.4 | **25.2** |
+|CCMT2019 dev | -  | 15.9 | 12.0 | - | 14.8 | - | **19.6** |
+
+
+## 🏁 Getting Started
+
+Please visit our [GitHub](https://github.com/MooreThreads/MooER) for the setup and usage.
+
+
+## 🧾 License
+
+Please see the [LICENSE](LICENSE).
+
+
+## 💖 Citation
+
+If you find MooER useful for your research, please 🌟 this repo and cite our work using the following BibTeX:
+
+```bibtex
+@article{liang2024mooer,
+  title   = {MooER: an LLM-based Speech Recognition and Translation Model from Moore Threads},
+  author  = {Zhenlin Liang, Junhao Xu, Yi Liu, Yichao Hu, Jian Li, Yajun Zheng, Meng Cai, Hua Wang},
+  journal = {arXiv preprint arXiv:2408.05101},
+  url     = {https://arxiv.org/abs/2408.05101}, 
+  year    = {2024}
+}
+```
+
+## 📧 Contact
+
+If you encouter any problems, feel free to create a discussion.
+
+Moore Threads Website: **https://www.mthreads.com/**
+
+<br>
+<p align="left">
+    <img src="assets/MTLogo.png" width="300"/>
+<p>
+<br>

asr/adapter_project.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3e549b5d3b460ce64ed85341687093432264d4412f4bbd543b6823b046ecc93
+size 37772943

asr/lora_weights/README.md

@@ -0,0 +1,9 @@
+---
+library_name: peft
+---
+## Training procedure
+
+### Framework versions
+
+
+- PEFT 0.5.0

asr/lora_weights/adapter_config.json

@@ -0,0 +1,26 @@
+{
+  "auto_mapping": null,
+  "base_model_name_or_path": "/jfs/zhenlin.liang/model/Qwen2-7B-Instruct",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 16,
+  "lora_dropout": 0.05,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 128,
+  "revision": null,
+  "target_modules": [
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+    "o_proj",
+    "up_proj",
+    "gate_proj",
+    "down_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

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configuration.json

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