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Qwen3-ASR-0.6B

Automatic Speech Recognition
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  1. README.md +1393 -0
  2. chat_template.json +1 -0
  3. config.json +221 -0
  4. generation_config.json +7 -0
  5. merges.txt +0 -0
  6. model.safetensors +3 -0
  7. preprocessor_config.json +14 -0
  8. tokenizer_config.json +549 -0
  9. vocab.json +0 -0
README.md ADDED
@@ -0,0 +1,1393 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ pipeline_tag: automatic-speech-recognition
4
+ ---
5
+
6
+ # Qwen3-ASR
7
+
8
+ ## Overview
9
+
10
+ ### Introduction
11
+
12
+ <p align="center">
13
+ <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/qwen3_asr_introduction.png" width="90%"/>
14
+ <p>
15
+
16
+ The Qwen3-ASR family includes Qwen3-ASR-1.7B and Qwen3-ASR-0.6B, which support language identification and ASR for 52 languages and dialects. Both leverage large-scale speech training data and the strong audio understanding capability of their foundation model, Qwen3-Omni. Experiments show that the 1.7B version achieves state-of-the-art performance among open-source ASR models and is competitive with the strongest proprietary commercial APIs. Here are the main features:
17
+
18
+ * **All-in-one**: Qwen3-ASR-1.7B and Qwen3-ASR-0.6B support language identification and speech recognition for 30 languages and 22 Chinese dialects, so as to English accents from multiple countries and regions.
19
+
20
+ * **Excellent and Fast**: The Qwen3-ASR family ASR models maintains high-quality and robust recognition under complex acoustic environments and challenging text patterns. Qwen3-ASR-1.7B achieves strong performance on both open-sourced and internal benchmarks. While the 0.6B version achieves accuracy-efficient trade-off, it reaches 2000 times throughput at a concurrency of 128. They both achieve streaming / offline unified inference with single model and support transcribe long audio.
21
+
22
+ * **Novel and strong forced alignment Solution**: We introduce Qwen3-ForcedAligner-0.6B, which supports timestamp prediction for arbitrary units within up to 5 minutes of speech in 11 languages. Evaluations show its timestamp accuracy surpasses E2E based forced-alignment models.
23
+
24
+ * **Comprehensive inference toolkit**: In addition to open-sourcing the architectures and weights of the Qwen3-ASR series, we also release a powerful, full-featured inference framework that supports vLLM-based batch inference, asynchronous serving, streaming inference, timestamp prediction, and more.
25
+
26
+ ### Model Architecture
27
+
28
+ <p align="center">
29
+ <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/overview.jpg" width="100%"/>
30
+ <p>
31
+
32
+
33
+ ### Released Models Description and Download
34
+
35
+ Below is an introduction and download information for the Qwen3-ASR models. Please select and download the model that fits your needs.
36
+
37
+ | Model | Supported Languages | Supported Dialects | Inference Mode | Audio Types |
38
+ |---|---|---|---|---|
39
+ | Qwen3-ASR-1.7B & Qwen3-ASR-0.6B | Chinese (zh), English (en), Cantonese (yue), Arabic (ar), German (de), French (fr), Spanish (es), Portuguese (pt), Indonesian (id), Italian (it), Korean (ko), Russian (ru), Thai (th), Vietnamese (vi), Japanese (ja), Turkish (tr), Hindi (hi), Malay (ms), Dutch (nl), Swedish (sv), Danish (da), Finnish (fi), Polish (pl), Czech (cs), Filipino (fil), Persian (fa), Greek (el), Hungarian (hu), Macedonian (mk), Romanian (ro) | Anhui, Dongbei, Fujian, Gansu, Guizhou, Hebei, Henan, Hubei, Hunan, Jiangxi, Ningxia, Shandong, Shaanxi, Shanxi, Sichuan, Tianjin, Yunnan, Zhejiang, Cantonese (Hong Kong accent), Cantonese (Guangdong accent), Wu language, Minnan language. | Offline / Streaming | Speech, Singing Voice, Songs with BGM |
40
+ | Qwen3-ForcedAligner-0.6B | Chinese, English, Cantonese, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish | -- | NAR | Speech |
41
+
42
+ During model loading in the `qwen-asr` package or vLLM, model weights will be downloaded automatically based on the model name. However, if your runtime environment does not allow downloading weights during execution, you can use the following commands to manually download the model weights to a local directory:
43
+
44
+ ```bash
45
+ # Download through ModelScope (recommended for users in Mainland China)
46
+ pip install -U modelscope
47
+ modelscope download --model Qwen/Qwen3-ASR-1.7B --local_dir ./Qwen3-ASR-1.7B
48
+ modelscope download --model Qwen/Qwen3-ASR-0.6B --local_dir ./Qwen3-ASR-0.6B
49
+ modelscope download --model Qwen/Qwen3-ForcedAligner-0.6B --local_dir ./Qwen3-ForcedAligner-0.6B
50
+ # Download through Hugging Face
51
+ pip install -U "huggingface_hub[cli]"
52
+ huggingface-cli download Qwen/Qwen3-ASR-1.7B --local-dir ./Qwen3-ASR-1.7B
53
+ huggingface-cli download Qwen/Qwen3-ASR-0.6B --local-dir ./Qwen3-ASR-0.6B
54
+ huggingface-cli download Qwen/Qwen3-ForcedAligner-0.6B --local-dir ./Qwen3-ForcedAligner-0.6B
55
+ ```
56
+
57
+
58
+ ## Quickstart
59
+
60
+ ### Environment Setup
61
+
62
+ The easiest way to use Qwen3-ASR is to install the `qwen-asr` Python package from PyPI. This will pull in the required runtime dependencies and allow you to load any released Qwen3-ASR model. If you’d like to simplify environment setup further, you can also use our official [Docker image](#docker). The `qwen-asr` package provides two backends: the transformers backend and the vLLM backend. For usage instructions for different backends, please refer to [Python Package Usage](#python-package-usage). We recommend using a **fresh, isolated environment** to avoid dependency conflicts with existing packages. You can create a clean Python 3.12 environment like this:
63
+
64
+ ```bash
65
+ conda create -n qwen3-asr python=3.12 -y
66
+ conda activate qwen3-asr
67
+ ```
68
+
69
+ Run the following command to get the minimal installation with transformers-backend support:
70
+
71
+ ```bash
72
+ pip install -U qwen-asr
73
+ ```
74
+
75
+ To enable the vLLM backend for faster inference and streaming support, run:
76
+
77
+ ```bash
78
+ pip install -U qwen-asr[vllm]
79
+ ```
80
+
81
+ If you want to develop or modify the code locally, install from source in editable mode:
82
+
83
+ ```bash
84
+ git clone https://github.com/QwenLM/Qwen3-ASR.git
85
+ cd Qwen3-ASR
86
+ pip install -e .
87
+ # support vLLM backend
88
+ # pip install -e ".[vllm]"
89
+ ```
90
+
91
+ Additionally, we recommend using FlashAttention 2 to reduce GPU memory usage and accelerate inference speed, especially for long inputs and large batch sizes.
92
+
93
+ ```bash
94
+ pip install -U flash-attn --no-build-isolation
95
+ ```
96
+
97
+ If your machine has less than 96GB of RAM and lots of CPU cores, run:
98
+
99
+ ```bash
100
+ MAX_JOBS=4 pip install -U flash-attn --no-build-isolation
101
+ ```
102
+
103
+ Also, you should have hardware that is compatible with FlashAttention 2. Read more about it in the official documentation of the [FlashAttention repository](https://github.com/Dao-AILab/flash-attention). FlashAttention 2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`.
104
+
105
+ ### Python Package Usage
106
+
107
+ #### Quick Inference
108
+
109
+ The `qwen-asr` package provides two backends: **transformers backend** and **vLLM backend**. You can pass audio inputs as a local path, a URL, base64 data, or a `(np.ndarray, sr)` tuple, and run batch inference. To quickly try Qwen3-ASR, you can use `Qwen3ASRModel.from_pretrained(...)` for the transformers backend with the following code:
110
+
111
+ ```python
112
+ import torch
113
+ from qwen_asr import Qwen3ASRModel
114
+
115
+ model = Qwen3ASRModel.from_pretrained(
116
+ "Qwen/Qwen3-ASR-1.7B",
117
+ dtype=torch.bfloat16,
118
+ device_map="cuda:0",
119
+ # attn_implementation="flash_attention_2",
120
+ max_inference_batch_size=32, # Batch size limit for inference. -1 means unlimited. Smaller values can help avoid OOM.
121
+ max_new_tokens=256, # Maximum number of tokens to generate. Set a larger value for long audio input.
122
+ )
123
+
124
+ results = model.transcribe(
125
+ audio="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav",
126
+ language=None, # set "English" to force the language
127
+ )
128
+
129
+ print(results[0].language)
130
+ print(results[0].text)
131
+ ```
132
+
133
+ If you want to return timestamps, pass `forced_aligner` and its init kwargs. Here is an example of batch inference with timestamps output:
134
+
135
+ ```python
136
+ import torch
137
+ from qwen_asr import Qwen3ASRModel
138
+
139
+ model = Qwen3ASRModel.from_pretrained(
140
+ "Qwen/Qwen3-ASR-1.7B",
141
+ dtype=torch.bfloat16,
142
+ device_map="cuda:0",
143
+ # attn_implementation="flash_attention_2",
144
+ max_inference_batch_size=32, # Batch size limit for inference. -1 means unlimited. Smaller values can help avoid OOM.
145
+ max_new_tokens=256, # Maximum number of tokens to generate. Set a larger value for long audio input.
146
+ forced_aligner="Qwen/Qwen3-ForcedAligner-0.6B",
147
+ forced_aligner_kwargs=dict(
148
+ dtype=torch.bfloat16,
149
+ device_map="cuda:0",
150
+ # attn_implementation="flash_attention_2",
151
+ ),
152
+ )
153
+
154
+ results = model.transcribe(
155
+ audio=[
156
+ "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav",
157
+ "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav",
158
+ ],
159
+ language=["Chinese", "English"], # can also be set to None for automatic language detection
160
+ return_time_stamps=True,
161
+ )
162
+
163
+ for r in results:
164
+ print(r.language, r.text, r.time_stamps[0])
165
+ ```
166
+
167
+ For more detailed usage examples, please refer to the [example code](https://github.com/QwenLM/Qwen3-ASR/blob/main/examples/example_qwen3_asr_transformers.py) for the transformers backend.
168
+
169
+ #### vLLM Backend
170
+
171
+ If you want the fastest inference speed with Qwen3-ASR, we strongly recommend using the vLLM backend by initializing the model with `Qwen3ASRModel.LLM(...)`. Example code is provided below. Note that you must install it via `pip install -U qwen-asr[vllm]`. If you want the model to output timestamps, it’s best to install FlashAttention via `pip install -U flash-attn --no-build-isolation` to speed up inference for the forced aligner model. Remember to wrap your code under `if __name__ == '__main__':` to avoid the `spawn` error described in [vLLM Troubleshooting](https://docs.vllm.ai/en/latest/usage/troubleshooting/#python-multiprocessing).
172
+
173
+ ```python
174
+ import torch
175
+ from qwen_asr import Qwen3ASRModel
176
+
177
+ if __name__ == '__main__':
178
+ model = Qwen3ASRModel.LLM(
179
+ model="Qwen/Qwen3-ASR-1.7B",
180
+ gpu_memory_utilization=0.7,
181
+ max_inference_batch_size=128, # Batch size limit for inference. -1 means unlimited. Smaller values can help avoid OOM.
182
+ max_new_tokens=4096, # Maximum number of tokens to generate. Set a larger value for long audio input.
183
+ forced_aligner="Qwen/Qwen3-ForcedAligner-0.6B",
184
+ forced_aligner_kwargs=dict(
185
+ dtype=torch.bfloat16,
186
+ device_map="cuda:0",
187
+ # attn_implementation="flash_attention_2",
188
+ ),
189
+ )
190
+
191
+ results = model.transcribe(
192
+ audio=[
193
+ "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav",
194
+ "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav",
195
+ ],
196
+ language=["Chinese", "English"], # can also be set to None for automatic language detection
197
+ return_time_stamps=True,
198
+ )
199
+
200
+ for r in results:
201
+ print(r.language, r.text, r.time_stamps[0])
202
+ ```
203
+
204
+ For more detailed usage examples, please refer to the [example code](https://github.com/QwenLM/Qwen3-ASR/blob/main/examples/example_qwen3_asr_vllm.py) for the vLLM backend. In addition, you can start a vLLM server via the `qwen-asr-serve` command, which is a wrapper around `vllm serve`. You can pass any arguments supported by `vllm serve`, for example:
205
+
206
+ ```bash
207
+ qwen-asr-serve Qwen/Qwen3-ASR-1.7B --gpu-memory-utilization 0.8 --host 0.0.0.0 --port 8000
208
+ ```
209
+
210
+ And send requests to the server via:
211
+
212
+ ```python
213
+ import requests
214
+
215
+ url = "http://localhost:8000/v1/chat/completions"
216
+ headers = {"Content-Type": "application/json"}
217
+
218
+ data = {
219
+ "messages": [
220
+ {
221
+ "role": "user",
222
+ "content": [
223
+ {
224
+ "type": "audio_url",
225
+ "audio_url": {
226
+ "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav"
227
+ },
228
+ }
229
+ ],
230
+ }
231
+ ]
232
+ }
233
+
234
+ response = requests.post(url, headers=headers, json=data, timeout=300)
235
+ response.raise_for_status()
236
+ content = response.json()['choices'][0]['message']['content']
237
+ print(content)
238
+
239
+ # parse ASR output if you want
240
+ from qwen_asr import parse_asr_output
241
+ language, text = parse_asr_output(content)
242
+ print(language)
243
+ print(text)
244
+ ```
245
+
246
+ #### Streaming Inference
247
+
248
+ Qwen3-ASR fully supports streaming inference. Currently, streaming inference is only available with the vLLM backend. Note that streaming inference does not support batch inference or returning timestamps. Please refer to the [example code](https://github.com/QwenLM/Qwen3-ASR/blob/main/examples/example_qwen3_asr_vllm_streaming.py) for details. You can also launch a streaming web demo through the [guide](#streaming-demo) to experience Qwen3-ASR’s streaming transcription capabilities.
249
+
250
+ #### ForcedAligner Usage
251
+
252
+ `Qwen3-ForcedAligner-0.6B` can align text–speech pairs and return word or character level timestamps. Here is an example of using the forced aligner directly:
253
+
254
+ ```python
255
+ import torch
256
+ from qwen_asr import Qwen3ForcedAligner
257
+
258
+ model = Qwen3ForcedAligner.from_pretrained(
259
+ "Qwen/Qwen3-ForcedAligner-0.6B",
260
+ dtype=torch.bfloat16,
261
+ device_map="cuda:0",
262
+ # attn_implementation="flash_attention_2",
263
+ )
264
+
265
+ results = model.align(
266
+ audio="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav",
267
+ text="甚至出现交易几乎停滞的情况。",
268
+ language="Chinese",
269
+ )
270
+
271
+ print(results[0])
272
+ print(results[0][0].text, results[0][0].start_time, results[0][0].end_time)
273
+ ```
274
+
275
+ In addition, the forced aligner supports local paths / URLs / base64 data / `(np.ndarray, sr)` inputs and batch inference. Please refer to the [example code](https://github.com/QwenLM/Qwen3-ASR/blob/main/examples/example_qwen3_forced_aligner.py) for details.
276
+
277
+ ### DashScope API Usage
278
+
279
+ To further explore Qwen3-ASR, we encourage you to try our DashScope API for a faster and more efficient experience. For detailed API information and documentation, please refer to the following:
280
+
281
+ | API Description | API Documentation (Mainland China) | API Documentation (International) |
282
+ |------------------|-----------------------------------|------------------------------------|
283
+ | Real-time API for Qwen3-ASR. | [https://help.aliyun.com/zh/model-studio/qwen-real-time-speech-recognition](https://help.aliyun.com/zh/model-studio/qwen-real-time-speech-recognition) | [https://www.alibabacloud.com/help/en/model-studio/qwen-real-time-speech-recognition](https://www.alibabacloud.com/help/en/model-studio/qwen-real-time-speech-recognition) |
284
+ | FileTrans API for Qwen3-ASR. | [https://help.aliyun.com/zh/model-studio/qwen-speech-recognition](https://help.aliyun.com/zh/model-studio/qwen-speech-recognition) | [https://www.alibabacloud.com/help/en/model-studio/qwen-speech-recognition](https://www.alibabacloud.com/help/en/model-studio/qwen-speech-recognition) |
285
+
286
+
287
+ ## Launch Local Web UI Demo
288
+
289
+ ### Gradio Demo
290
+
291
+ To launch the Qwen3-ASR web UI gradio demo, install the `qwen-asr` package and run `qwen-asr-demo`. Use the command below for help:
292
+
293
+ ```bash
294
+ qwen-asr-demo --help
295
+ ```
296
+
297
+ To launch the demo, you can use the following commands:
298
+
299
+ ```bash
300
+ # Transformers backend
301
+ qwen-asr-demo \
302
+ --asr-checkpoint Qwen/Qwen3-ASR-1.7B \
303
+ --backend transformers \
304
+ --cuda-visible-devices 0 \
305
+ --ip 0.0.0.0 --port 8000
306
+
307
+ # Transformers backend + Forced Aligner (enable timestamps)
308
+ qwen-asr-demo \
309
+ --asr-checkpoint Qwen/Qwen3-ASR-1.7B \
310
+ --aligner-checkpoint Qwen/Qwen3-ForcedAligner-0.6B \
311
+ --backend transformers \
312
+ --cuda-visible-devices 0 \
313
+ --backend-kwargs '{"device_map":"cuda:0","dtype":"bfloat16","max_inference_batch_size":8,"max_new_tokens":256}' \
314
+ --aligner-kwargs '{"device_map":"cuda:0","dtype":"bfloat16"}' \
315
+ --ip 0.0.0.0 --port 8000
316
+
317
+ # vLLM backend + Forced Aligner (enable timestamps)
318
+ qwen-asr-demo \
319
+ --asr-checkpoint Qwen/Qwen3-ASR-1.7B \
320
+ --aligner-checkpoint Qwen/Qwen3-ForcedAligner-0.6B \
321
+ --backend vllm \
322
+ --cuda-visible-devices 0 \
323
+ --backend-kwargs '{"gpu_memory_utilization":0.7,"max_inference_batch_size":8,"max_new_tokens":2048}' \
324
+ --aligner-kwargs '{"device_map":"cuda:0","dtype":"bfloat16"}' \
325
+ --ip 0.0.0.0 --port 8000
326
+ ```
327
+
328
+ Then open `http://<your-ip>:8000`, or access it via port forwarding in tools like VS Code.
329
+
330
+ #### Backend Notes
331
+
332
+ This demo supports two backends: transformers and vLLM. All backend-specific initialization parameters should be passed via `--backend-kwargs` as a JSON dict. If not provided, the demo will use sensible defaults.
333
+
334
+ ```bash
335
+ # Example: override transformers init args without flash attention
336
+ --backend-kwargs '{"device_map":"cuda:0","dtype":"bfloat16"}'
337
+
338
+ # Example: override vLLM init args with 65% GPU memory
339
+ --backend-kwargs '{"gpu_memory_utilization":0.65}'
340
+ ```
341
+
342
+ #### CUDA Device Notes
343
+
344
+ Because vLLM does not follow `cuda:0` style device selection, this demo selects GPUs by setting `CUDA_VISIBLE_DEVICES` via `--cuda-visible-devices`.
345
+
346
+ ```bash
347
+ # Use GPU 0
348
+ --cuda-visible-devices 0
349
+
350
+ # Use GPU 1
351
+ --cuda-visible-devices 1
352
+ ```
353
+
354
+ #### Timestamps Notes
355
+
356
+ Timestamps are only available when `--aligner-checkpoint` is provided. If you launch the demo without a forced aligner, the timestamps UI will be hidden automatically.
357
+
358
+ ```bash
359
+ # No forced aligner
360
+ qwen-asr-demo --asr-checkpoint Qwen/Qwen3-ASR-1.7B
361
+
362
+ # With forced aligner
363
+ qwen-asr-demo \
364
+ --asr-checkpoint Qwen/Qwen3-ASR-1.7B \
365
+ --aligner-checkpoint Qwen/Qwen3-ForcedAligner-0.6B
366
+ ```
367
+
368
+ #### HTTPS Notes
369
+
370
+ To avoid browser microphone permission issues after deploying the server, it is recommended/required to run the gradio service over HTTPS (especially when accessed remotely or behind modern browsers/gateways). Use `--ssl-certfile` and `--ssl-keyfile` to enable HTTPS. First, generate a private key and a self-signed certificate (valid for 365 days):
371
+
372
+ ```bash
373
+ openssl req -x509 -newkey rsa:2048 \
374
+ -keyout key.pem -out cert.pem \
375
+ -days 365 -nodes \
376
+ -subj "/CN=localhost"
377
+ ```
378
+
379
+ Then run the demo with HTTPS:
380
+
381
+ ```bash
382
+ qwen-asr-demo \
383
+ --asr-checkpoint Qwen/Qwen3-ASR-1.7B \
384
+ --backend transformers \
385
+ --cuda-visible-devices 0 \
386
+ --ip 0.0.0.0 --port 8000 \
387
+ --ssl-certfile cert.pem \
388
+ --ssl-keyfile key.pem \
389
+ --no-ssl-verify
390
+ ```
391
+
392
+ Then open `https://<your-ip>:8000` to use it. If your browser shows a warning, that’s expected for self-signed certificates. For production, use a real certificate.
393
+
394
+ ### Streaming Demo
395
+
396
+ To experience Qwen3-ASR’s streaming transcription capability in a web UI, we provide a minimal Flask-based streaming demo. The demo captures microphone audio in the browser, resamples it to 16,000 Hz, and continuously pushes PCM chunks to the model. Run the demo with the following command:
397
+
398
+ ```bash
399
+ qwen-asr-demo-streaming \
400
+ --asr-model-path Qwen/Qwen3-ASR-1.7B \
401
+ --host 0.0.0.0 \
402
+ --port 8000 \
403
+ --gpu-memory-utilization 0.9
404
+ ```
405
+
406
+ Then open `http://<your-ip>:8000`, or access it via port forwarding in tools like VS Code.
407
+
408
+ ## Deployment with vLLM
409
+
410
+ vLLM officially provides day-0 model support for Qwen3-ASR for efficient inference.
411
+
412
+ ### Installation
413
+ You can run Qwen3-ASR with vLLM nightly wheel or docker image. To install the nightly version of vLLM, we recommend using `uv` as the environment manager
414
+ ```bash
415
+ uv venv
416
+ source .venv/bin/activate
417
+ uv pip install -U vllm --pre \
418
+ --extra-index-url https://wheels.vllm.ai/nightly/cu129 \
419
+ --extra-index-url https://download.pytorch.org/whl/cu129 \
420
+ --index-strategy unsafe-best-match
421
+ uv pip install "vllm[audio]" # For additional audio dependencies
422
+ ```
423
+
424
+ ### Online Serving
425
+ You can easily deploy Qwen3-ASR with vLLM by running the following command
426
+ ```bash
427
+ vllm serve Qwen/Qwen3-ASR-1.7B
428
+ ```
429
+ After the model server is successfully deployed, you can interact with it in multiple ways.
430
+
431
+ #### Using OpenAI SDK
432
+ ```python
433
+ import base64
434
+ import httpx
435
+ from openai import OpenAI
436
+
437
+ # Initialize client
438
+ client = OpenAI(
439
+ base_url="http://localhost:8000/v1",
440
+ api_key="EMPTY"
441
+ )
442
+
443
+ # Create multimodal chat completion request
444
+ response = client.chat.completions.create(
445
+ model="Qwen/Qwen3-ASR-1.7B",
446
+ messages=[
447
+ {
448
+ "role": "user",
449
+ "content": [
450
+ {
451
+ "type": "audio_url",
452
+ "audio_url": {
453
+ {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav"}
454
+ }
455
+ }
456
+ ]
457
+ }
458
+ ],
459
+ )
460
+
461
+ print(response.choices[0].message.content)
462
+ ```
463
+ This model is also supported on vLLM with OpenAI transcription API.
464
+ ```python
465
+ import httpx
466
+ from openai import OpenAI
467
+
468
+ # Initialize client
469
+ client = OpenAI(
470
+ base_url="http://localhost:8000/v1",
471
+ api_key="EMPTY"
472
+ )
473
+ audio_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav"
474
+ audio_file = httpx.get(audio_url).content
475
+
476
+ transcription = client.audio.transcriptions.create(
477
+ model="Qwen/Qwen3-ASR-1.7B",
478
+ file=audio_file,
479
+ )
480
+
481
+ print(transcription.text)
482
+ ```
483
+
484
+ #### Using cURL
485
+ ```bash
486
+ curl http://localhost:8000/v1/chat/completions \
487
+ -H "Content-Type: application/json" \
488
+ -d '{
489
+ "messages": [
490
+ {"role": "user", "content": [
491
+ {"type": "audio_url", "audio_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav"}}
492
+ ]}
493
+ ]
494
+ }'
495
+ ```
496
+
497
+ ### Offline Inference
498
+ See the following example on using vLLM to run offline inference with Qwen3-ASR
499
+ ```python
500
+ from vllm import LLM, SamplingParams
501
+ from vllm.assets.audio import AudioAsset
502
+ import base64
503
+ import requests
504
+
505
+ # Initialize the LLM
506
+ llm = LLM(
507
+ model="Qwen/Qwen3-ASR-1.7B"
508
+ )
509
+
510
+ # Load audio
511
+ audio_asset = AudioAsset("winning_call")
512
+
513
+ # Create conversation with audio content
514
+ conversation = [
515
+ {
516
+ "role": "user",
517
+ "content": [
518
+ {
519
+ "type": "audio_url",
520
+ "audio_url": {"url": audio_asset.url}
521
+ }
522
+ ]
523
+ }
524
+ ]
525
+
526
+ sampling_params = SamplingParams(temperature=0.01, max_tokens=256)
527
+
528
+ # Run inference using .chat()
529
+ outputs = llm.chat(conversation, sampling_params=sampling_params)
530
+ print(outputs[0].outputs[0].text)
531
+ ```
532
+
533
+
534
+ ## Docker
535
+
536
+ To make it easier to use our `qwen-asr` Python package, we provide a pre-built Docker image: [qwenllm/qwen3-asr](https://hub.docker.com/r/qwenllm/qwen3-asr). You only need to install the GPU driver and download the model files to run the code. Please follow the [NVIDIA Container Toolkit installation guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) to ensure Docker can access your GPU. If you are in Mainland China and have trouble reaching Docker Hub, you may use a registry mirror to accelerate image pulls.
537
+
538
+ First, pull the image and start a container:
539
+
540
+ ```bash
541
+ LOCAL_WORKDIR=/path/to/your/workspace
542
+ HOST_PORT=8000
543
+ CONTAINER_PORT=80
544
+ docker run --gpus all --name qwen3-asr \
545
+ -v /var/run/docker.sock:/var/run/docker.sock -p $HOST_PORT:$CONTAINER_PORT \
546
+ --mount type=bind,source=$LOCAL_WORKDIR,target=/data/shared/Qwen3-ASR \
547
+ --shm-size=4gb \
548
+ -it qwenllm/qwen3-asr:latest
549
+ ```
550
+
551
+ After running the command, you will enter the container’s bash shell. Your local workspace (**replace** `/path/to/your/workspace` **with the actual path**) will be mounted inside the container at `/data/shared/Qwen3-ASR`. Port `8000` on the host is mapped to port `80` in the container, so you can access services running in the container via `http://<host-ip>:8000`. Note that services inside the container must bind to `0.0.0.0` (not `127.0.0.1`) for port forwarding to work.
552
+
553
+ If you exit the container, you can start it again and re-enter it with:
554
+
555
+ ```bash
556
+ docker start qwen3-asr
557
+ docker exec -it qwen3-asr bash
558
+ ```
559
+
560
+ To remove the container completely, run:
561
+
562
+ ```bash
563
+ docker rm -f qwen3-asr
564
+ ```
565
+
566
+
567
+ ## Evaluation
568
+
569
+ During evaluation, we ran inference for all models with `dtype=torch.bfloat16` and set `max_new_tokens=1024` using vLLM. Greedy search was used for all decoding, and none of the tests specified a language parameter. The detailed evaluation results are shown below.
570
+
571
+ <details>
572
+ <summary>ASR Benchmarks on Public Datasets (WER ↓)</summary>
573
+
574
+ <table>
575
+ <thead>
576
+ <tr>
577
+ <th colspan="2" style="text-align: left;"></th>
578
+ <th style="text-align: center;">GPT-4o<br>-Transcribe</th>
579
+ <th style="text-align: center;">Gemini-2.5<br>-Pro</th>
580
+ <th style="text-align: center;">Doubao-ASR</th>
581
+ <th style="text-align: center;">Whisper<br>-large-v3</th>
582
+ <th style="text-align: center;">Fun-ASR<br>-MLT-Nano</th>
583
+ <th style="text-align: center;">Qwen3-ASR<br>-0.6B</th>
584
+ <th style="text-align: center;">Qwen3-ASR<br>-1.7B</th>
585
+ </tr>
586
+ </thead>
587
+ <tbody>
588
+ <tr>
589
+ <td colspan="9" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">English (en)</td>
590
+ </tr>
591
+ <tr>
592
+ <td colspan="2" style="text-align: left;">Librispeech<br>clean | other</td>
593
+ <td style="text-align: center;"><strong>1.39</strong> | 3.75</td>
594
+ <td style="text-align: center;">2.89 | 3.56</td>
595
+ <td style="text-align: center;">2.78 | 5.70</td>
596
+ <td style="text-align: center;">1.51 | 3.97</td>
597
+ <td style="text-align: center;">1.68 | 4.03</td>
598
+ <td style="text-align: center;">2.11 | 4.55</td>
599
+ <td style="text-align: center;">1.63 | <strong>3.38</strong></td>
600
+ </tr>
601
+ <tr>
602
+ <td colspan="2" style="text-align: left;">GigaSpeech</td>
603
+ <td style="text-align: center;">25.50</td>
604
+ <td style="text-align: center;">9.37</td>
605
+ <td style="text-align: center;">9.55</td>
606
+ <td style="text-align: center;">9.76</td>
607
+ <td style="text-align: center;">-</td>
608
+ <td style="text-align: center;">8.88</td>
609
+ <td style="text-align: center;"><strong>8.45</strong></td>
610
+ </tr>
611
+ <tr>
612
+ <td colspan="2" style="text-align: left;">CV-en</td>
613
+ <td style="text-align: center;">9.08</td>
614
+ <td style="text-align: center;">14.49</td>
615
+ <td style="text-align: center;">13.78</td>
616
+ <td style="text-align: center;">9.90</td>
617
+ <td style="text-align: center;">9.90</td>
618
+ <td style="text-align: center;">9.92</td>
619
+ <td style="text-align: center;"><strong>7.39</strong></td>
620
+ </tr>
621
+ <tr>
622
+ <td colspan="2" style="text-align: left;">Fleurs-en</td>
623
+ <td style="text-align: center;"><strong>2.40</strong></td>
624
+ <td style="text-align: center;">2.94</td>
625
+ <td style="text-align: center;">6.31</td>
626
+ <td style="text-align: center;">4.08</td>
627
+ <td style="text-align: center;">5.49</td>
628
+ <td style="text-align: center;">4.39</td>
629
+ <td style="text-align: center;">3.35</td>
630
+ </tr>
631
+ <tr>
632
+ <td colspan="2" style="text-align: left;">MLS-en</td>
633
+ <td style="text-align: center;">5.12</td>
634
+ <td style="text-align: center;"><strong>3.68</strong></td>
635
+ <td style="text-align: center;">7.09</td>
636
+ <td style="text-align: center;">4.87</td>
637
+ <td style="text-align: center;">-</td>
638
+ <td style="text-align: center;">6.00</td>
639
+ <td style="text-align: center;">4.58</td>
640
+ </tr>
641
+ <tr>
642
+ <td colspan="2" style="text-align: left;">Tedlium</td>
643
+ <td style="text-align: center;">7.69</td>
644
+ <td style="text-align: center;">6.15</td>
645
+ <td style="text-align: center;">4.91</td>
646
+ <td style="text-align: center;">6.84</td>
647
+ <td style="text-align: center;">-</td>
648
+ <td style="text-align: center;"><strong>3.85<strong></td>
649
+ <td style="text-align: center;"><strong>4.50</strong></td>
650
+ </tr>
651
+ <tr>
652
+ <td colspan="2" style="text-align: left;">VoxPopuli</td>
653
+ <td style="text-align: center;">10.29</td>
654
+ <td style="text-align: center;">11.36</td>
655
+ <td style="text-align: center;">12.12</td>
656
+ <td style="text-align: center;">12.05</td>
657
+ <td style="text-align: center;">-</td>
658
+ <td style="text-align: center;"><strong>9.96<strong></td>
659
+ <td style="text-align: center;"><strong>9.15</strong></td>
660
+ </tr>
661
+ <tr>
662
+ <td colspan="9" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">Chinese (zh)</td>
663
+ </tr>
664
+ <tr>
665
+ <td colspan="2" style="text-align: left;">WenetSpeech<br>net | meeting</td>
666
+ <td style="text-align: center;">15.30 | 32.27</td>
667
+ <td style="text-align: center;">14.43 | 13.47</td>
668
+ <td style="text-align: center;">N/A</td>
669
+ <td style="text-align: center;">9.86 | 19.11</td>
670
+ <td style="text-align: center;">6.35 | -</td>
671
+ <td style="text-align: center;">5.97 | 6.88</td>
672
+ <td style="text-align: center;"><strong>4.97</strong> | <strong>5.88</strong></td>
673
+ </tr>
674
+ <tr>
675
+ <td colspan="2" style="text-align: left;">AISHELL-2-test</td>
676
+ <td style="text-align: center;">4.24</td>
677
+ <td style="text-align: center;">11.62</td>
678
+ <td style="text-align: center;">2.85</td>
679
+ <td style="text-align: center;">5.06</td>
680
+ <td style="text-align: center;">-</td>
681
+ <td style="text-align: center;">3.15</td>
682
+ <td style="text-align: center;"><strong>2.71</strong></td>
683
+ </tr>
684
+ <tr>
685
+ <td colspan="2" style="text-align: left;">SpeechIO</td>
686
+ <td style="text-align: center;">12.86</td>
687
+ <td style="text-align: center;">5.30</td>
688
+ <td style="text-align: center;">2.93</td>
689
+ <td style="text-align: center;">7.56</td>
690
+ <td style="text-align: center;">-</td>
691
+ <td style="text-align: center;">3.44</td>
692
+ <td style="text-align: center;"><strong>2.88</strong></td>
693
+ </tr>
694
+ <tr>
695
+ <td colspan="2" style="text-align: left;">Fleurs-zh</td>
696
+ <td style="text-align: center;">2.44</td>
697
+ <td style="text-align: center;">2.71</td>
698
+ <td style="text-align: center;">2.69</td>
699
+ <td style="text-align: center;">4.09</td>
700
+ <td style="text-align: center;">3.51</td>
701
+ <td style="text-align: center;">2.88</td>
702
+ <td style="text-align: center;"><strong>2.41</strong></td>
703
+ </tr>
704
+ <tr>
705
+ <td colspan="2" style="text-align: left;">CV-zh</td>
706
+ <td style="text-align: center;">6.32</td>
707
+ <td style="text-align: center;">7.70</td>
708
+ <td style="text-align: center;">5.95</td>
709
+ <td style="text-align: center;">12.91</td>
710
+ <td style="text-align: center;">6.20</td>
711
+ <td style="text-align: center;">6.89</td>
712
+ <td style="text-align: center;"><strong>5.35</strong></td>
713
+ </tr>
714
+ <tr>
715
+ <td colspan="9" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">Chinese Dialect</td>
716
+ </tr>
717
+ <tr>
718
+ <td colspan="2" style="text-align: left;">KeSpeech</td>
719
+ <td style="text-align: center;">26.87</td>
720
+ <td style="text-align: center;">24.71</td>
721
+ <td style="text-align: center;">5.27</td>
722
+ <td style="text-align: center;">28.79</td>
723
+ <td style="text-align: center;">-</td>
724
+ <td style="text-align: center;">7.08</td>
725
+ <td style="text-align: center;"><strong>5.10</strong></td>
726
+ </tr>
727
+ <tr>
728
+ <td colspan="2" style="text-align: left;">Fleurs-yue</td>
729
+ <td style="text-align: center;">4.98</td>
730
+ <td style="text-align: center;">9.43</td>
731
+ <td style="text-align: center;">4.98</td>
732
+ <td style="text-align: center;">9.18</td>
733
+ <td style="text-align: center;">-</td>
734
+ <td style="text-align: center;">5.79</td>
735
+ <td style="text-align: center;"><strong>3.98</strong></td>
736
+ </tr>
737
+ <tr>
738
+ <td colspan="2" style="text-align: left;">CV-yue</td>
739
+ <td style="text-align: center;">11.36</td>
740
+ <td style="text-align: center;">18.76</td>
741
+ <td style="text-align: center;">13.20</td>
742
+ <td style="text-align: center;">16.23</td>
743
+ <td style="text-align: center;">-</td>
744
+ <td style="text-align: center;">9.50</td>
745
+ <td style="text-align: center;"><strong>7.57</strong></td>
746
+ </tr>
747
+ <tr>
748
+ <td colspan="2" style="text-align: left;">CV-zh-tw</td>
749
+ <td style="text-align: center;">6.32</td>
750
+ <td style="text-align: center;">7.31</td>
751
+ <td style="text-align: center;">4.06</td>
752
+ <td style="text-align: center;">7.84</td>
753
+ <td style="text-align: center;">-</td>
754
+ <td style="text-align: center;">5.59</td>
755
+ <td style="text-align: center;"><strong>3.77</strong></td>
756
+ </tr>
757
+ <tr>
758
+ <td colspan="2" style="text-align: left;">WenetSpeech-Yue<br>short | long</td>
759
+ <td style="text-align: center;">15.62 | 25.29</td>
760
+ <td style="text-align: center;">25.19 | 11.23</td>
761
+ <td style="text-align: center;">9.74 | 11.40</td>
762
+ <td style="text-align: center;">32.26 | 46.64</td>
763
+ <td style="text-align: center;">- | -</td>
764
+ <td style="text-align: center;">7.54 | 9.92</td>
765
+ <td style="text-align: center;"><strong>5.82</strong> | <strong>8.85</strong></td>
766
+ </tr>
767
+ <tr>
768
+ <td colspan="2" style="text-align: left;">WenetSpeech-Chuan<br>easy | hard</td>
769
+ <td style="text-align: center;">34.81 | 53.98</td>
770
+ <td style="text-align: center;">43.79 | 67.30</td>
771
+ <td style="text-align: center;"><strong>11.40<strong> | <strong>20.20</strong></td>
772
+ <td style="text-align: center;">14.35 | 26.80</td>
773
+ <td style="text-align: center;">- | -</td>
774
+ <td style="text-align: center;">13.92 | 24.45</td>
775
+ <td style="text-align: center;">11.99 | 21.63</td>
776
+ </tr>
777
+ </tbody>
778
+ </table>
779
+
780
+ </details>
781
+
782
+ <details>
783
+ <summary>ASR Benchmarks on Internal Datasets (WER ↓)</summary>
784
+
785
+ <table>
786
+ <thead>
787
+ <tr>
788
+ <th style="text-align: left;"></th>
789
+ <th style="text-align: center;">GPT-4o<br>-Transcribe</th>
790
+ <th style="text-align: center;">Gemini-2.5<br>-Pro</th>
791
+ <th style="text-align: center;">Doubao-ASR</th>
792
+ <th style="text-align: center;">Whisper<br>-large-v3</th>
793
+ <th style="text-align: center;">Fun-ASR<br>-MLT-Nano</th>
794
+ <th style="text-align: center;">Qwen3-ASR<br>-0.6B</th>
795
+ <th style="text-align: center;">Qwen3-ASR<br>-1.7B</th>
796
+ </tr>
797
+ </thead>
798
+ <tbody>
799
+ <tr>
800
+ <td colspan="8" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">Accented English</td>
801
+ </tr>
802
+ <tr>
803
+ <td style="text-align: left;">Dialog-Accented English</td>
804
+ <td style="text-align: center;">28.56</td>
805
+ <td style="text-align: center;">23.85</td>
806
+ <td style="text-align: center;">20.41</td>
807
+ <td style="text-align: center;">21.30</td>
808
+ <td style="text-align: center;">19.96</td>
809
+ <td style="text-align: center;"><strong>16.62<strong></td>
810
+ <td style="text-align: center;"><strong>16.07</strong></td>
811
+ </tr>
812
+ <tr>
813
+ <td colspan="8" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">Chinese Mandarin</td>
814
+ </tr>
815
+ <tr>
816
+ <td style="text-align: left;">Elders&Kids</td>
817
+ <td style="text-align: center;">14.27</td>
818
+ <td style="text-align: center;">36.93</td>
819
+ <td style="text-align: center;">4.17</td>
820
+ <td style="text-align: center;">10.61</td>
821
+ <td style="text-align: center;">4.54</td>
822
+ <td style="text-align: center;">4.48</td>
823
+ <td style="text-align: center;"><strong>3.81</strong></td>
824
+ </tr>
825
+ <tr>
826
+ <td style="text-align: left;">ExtremeNoise</td>
827
+ <td style="text-align: center;">36.11</td>
828
+ <td style="text-align: center;">29.06</td>
829
+ <td style="text-align: center;">17.04</td>
830
+ <td style="text-align: center;">63.17</td>
831
+ <td style="text-align: center;">36.55</td>
832
+ <td style="text-align: center;">17.88</td>
833
+ <td style="text-align: center;"><strong>16.17</strong></td>
834
+ </tr>
835
+ <tr>
836
+ <td style="text-align: left;">TongueTwister</td>
837
+ <td style="text-align: center;">20.87</td>
838
+ <td style="text-align: center;">4.97</td>
839
+ <td style="text-align: center;">3.47</td>
840
+ <td style="text-align: center;">16.63</td>
841
+ <td style="text-align: center;">9.02</td>
842
+ <td style="text-align: center;">4.06</td>
843
+ <td style="text-align: center;"><strong>2.44</strong></td>
844
+ </tr>
845
+ <tr>
846
+ <td style="text-align: left;">Dialog-Mandarin</td>
847
+ <td style="text-align: center;">20.73</td>
848
+ <td style="text-align: center;">12.50</td>
849
+ <td style="text-align: center;">6.61</td>
850
+ <td style="text-align: center;">14.01</td>
851
+ <td style="text-align: center;">7.32</td>
852
+ <td style="text-align: center;">7.06</td>
853
+ <td style="text-align: center;"><strong>6.54</strong></td>
854
+ </tr>
855
+ <tr>
856
+ <td colspan="8" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">Chinese Dialect</td>
857
+ </tr>
858
+ <tr>
859
+ <td style="text-align: left;">Dialog-Cantonese</td>
860
+ <td style="text-align: center;">16.05</td>
861
+ <td style="text-align: center;">14.98</td>
862
+ <td style="text-align: center;">7.56</td>
863
+ <td style="text-align: center;">31.04</td>
864
+ <td style="text-align: center;">5.85</td>
865
+ <td style="text-align: center;"><strong>4.80<strong></td>
866
+ <td style="text-align: center;"><strong>4.12</strong></td>
867
+ </tr>
868
+ <tr>
869
+ <td style="text-align: left;">Dialog-Chinese Dialects</td>
870
+ <td style="text-align: center;">45.37</td>
871
+ <td style="text-align: center;">47.70</td>
872
+ <td style="text-align: center;">19.85</td>
873
+ <td style="text-align: center;">44.55</td>
874
+ <td style="text-align: center;">19.41</td>
875
+ <td style="text-align: center;"><strong>18.24<strong></td>
876
+ <td style="text-align: center;"><strong>15.94</strong></td>
877
+ </tr>
878
+ </tbody>
879
+ </table>
880
+ <p><strong>Dialect coverage:</strong> Results for <em>Dialog-Accented English</em> are averaged over 16 accents, and results for <em>Dialog-Chinese Dialects</em> are averaged over 22 Chinese dialects.</p>
881
+
882
+ </details>
883
+
884
+ <details>
885
+ <summary>Multilingual ASR Benchmarks (WER ↓)</summary>
886
+
887
+ <table>
888
+ <thead>
889
+ <tr>
890
+ <th style="text-align: left;"></th>
891
+ <th style="text-align: center;">GLM-ASR<br>-Nano-2512</th>
892
+ <th style="text-align: center;">Whisper<br>-large-v3</th>
893
+ <th style="text-align: center;">Fun-ASR<br>-MLT-Nano</th>
894
+ <th style="text-align: center;">Qwen3-ASR<br>-0.6B</th>
895
+ <th style="text-align: center;">Qwen3-ASR<br>-1.7B</th>
896
+ </tr>
897
+ </thead>
898
+ <tbody>
899
+ <tr>
900
+ <td colspan="6" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">Open-sourced Benchmarks</td>
901
+ </tr>
902
+ <tr>
903
+ <td style="text-align: left;">MLS</td>
904
+ <td style="text-align: center;">13.32</td>
905
+ <td style="text-align: center;">8.62</td>
906
+ <td style="text-align: center;">28.70</td>
907
+ <td style="text-align: center;">13.19</td>
908
+ <td style="text-align: center;"><strong>8.55</strong></td>
909
+ </tr>
910
+ <tr>
911
+ <td style="text-align: left;">CommonVoice</td>
912
+ <td style="text-align: center;">19.40</td>
913
+ <td style="text-align: center;">10.77</td>
914
+ <td style="text-align: center;">17.25</td>
915
+ <td style="text-align: center;">12.75</td>
916
+ <td style="text-align: center;"><strong>9.18</strong></td>
917
+ </tr>
918
+ <tr>
919
+ <td style="text-align: left;">MLC-SLM</td>
920
+ <td style="text-align: center;">34.93</td>
921
+ <td style="text-align: center;">15.68</td>
922
+ <td style="text-align: center;">29.94</td>
923
+ <td style="text-align: center;">15.84</td>
924
+ <td style="text-align: center;"><strong>12.74</strong></td>
925
+ </tr>
926
+ <tr>
927
+ <td style="text-align: left;">Fleurs</td>
928
+ <td style="text-align: center;">16.08</td>
929
+ <td style="text-align: center;">5.27</td>
930
+ <td style="text-align: center;">10.03</td>
931
+ <td style="text-align: center;">7.57</td>
932
+ <td style="text-align: center;"><strong>4.90</strong></td>
933
+ </tr>
934
+ <tr>
935
+ <td style="text-align: left;">Fleurs<sup>†</sup></td>
936
+ <td style="text-align: center;">20.05</td>
937
+ <td style="text-align: center;">6.85</td>
938
+ <td style="text-align: center;">31.89</td>
939
+ <td style="text-align: center;">10.37</td>
940
+ <td style="text-align: center;"><strong>6.62</strong></td>
941
+ </tr>
942
+ <tr>
943
+ <td style="text-align: left;">Fleurs<sup>††</sup></td>
944
+ <td style="text-align: center;">24.83</td>
945
+ <td style="text-align: center;"><strong>8.16</strong></td>
946
+ <td style="text-align: center;">47.84</td>
947
+ <td style="text-align: center;">21.80</td>
948
+ <td style="text-align: center;">12.60</td>
949
+ </tr>
950
+ <tr>
951
+ <td colspan="6" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">Qwen-ASR Internal Benchmarks</td>
952
+ </tr>
953
+ <tr>
954
+ <td style="text-align: left;">News-Multilingual</td>
955
+ <td style="text-align: center;">49.40</td>
956
+ <td style="text-align: center;">14.80</td>
957
+ <td style="text-align: center;">65.07</td>
958
+ <td style="text-align: center;">17.39</td>
959
+ <td style="text-align: center;"><strong>12.80</strong></td>
960
+ </tr>
961
+ </tbody>
962
+ </table>
963
+ <p><strong>Language coverage:</strong> <em>MLS</em> includes 8 languages: {da, de, en, es, fr, it, pl, pt}.<br><em>CommonVoice</em> includes 13 languages: {en, zh, yue, zh_TW, ar, de, es, fr, it, ja, ko, pt, ru}.<br><em>MLC-SLM</em> includes 11 languages: {en, fr, de, it, pt, es, ja, ko, ru, th, vi}.<br><em>Fleurs</em> includes 12 languages: {en, zh, yue, ar, de, es, fr, it, ja, ko, pt, ru }.<br><em>Fleurs<sup>†</sup></em> includes 8 additional languages beyond Fleurs: {hi, id, ms, nl, pl, th, tr, vi}.<br><em>Fleurs<sup>††</sup></em> includes 10 additional languages beyond Fleurs<sup>†</sup>: {cs, da, el, fa, fi, fil, hu, mk, ro, sv}.<br><em>News-Multilingual</em> includes 15 languages: {ar, de, es, fr, hi, id, it, ja, ko, nl, pl, pt, ru, th, vi}.</p>
964
+
965
+ </details>
966
+
967
+ <details>
968
+ <summary>Language Identification Accuracy (%) ↑</summary>
969
+
970
+ <table>
971
+ <thead>
972
+ <tr>
973
+ <th style="text-align: left;"></th>
974
+ <th style="text-align: center;">Whisper-large-v3</th>
975
+ <th style="text-align: center;">Qwen3-ASR-0.6B</th>
976
+ <th style="text-align: center;">Qwen3-ASR-1.7B</th>
977
+ </tr>
978
+ </thead>
979
+ <tbody>
980
+ <tr>
981
+ <td style="text-align: left;">MLS</td>
982
+ <td style="text-align: center;"><strong>99.9</strong></td>
983
+ <td style="text-align: center;">99.3</td>
984
+ <td style="text-align: center;"><strong>99.9</strong></td>
985
+ </tr>
986
+ <tr>
987
+ <td style="text-align: left;">CommonVoice</td>
988
+ <td style="text-align: center;">92.7</td>
989
+ <td style="text-align: center;"><strong>98.2<strong></td>
990
+ <td style="text-align: center;"><strong>98.7</strong></td>
991
+ </tr>
992
+ <tr>
993
+ <td style="text-align: left;">MLC-SLM</td>
994
+ <td style="text-align: center;">89.2</td>
995
+ <td style="text-align: center;"><strong>92.7<strong></td>
996
+ <td style="text-align: center;"><strong>94.1</strong></td>
997
+ </tr>
998
+ <tr>
999
+ <td style="text-align: left;">Fleurs</td>
1000
+ <td style="text-align: center;">94.6</td>
1001
+ <td style="text-align: center;"><strong>97.1<strong></td>
1002
+ <td style="text-align: center;"><strong>98.7</strong></td>
1003
+ </tr>
1004
+ <tr style="border-top: 1px solid #ddd;">
1005
+ <td style="text-align: left;"><em>Avg.</em></td>
1006
+ <td style="text-align: center;">94.1</td>
1007
+ <td style="text-align: center;"><strong>96.8<strong></td>
1008
+ <td style="text-align: center;"><strong>97.9</strong></td>
1009
+ </tr>
1010
+ </tbody>
1011
+ </table>
1012
+ <p><strong>Language coverage:</strong> The language sets follow Multilingual ASR Benchmarks. Here, Fleurs corresponds to Fleurs<sup>††</sup> in Multilingual ASR Benchmarks and covers 30 languages.</p>
1013
+
1014
+ </details>
1015
+
1016
+ <details>
1017
+ <summary>Singing Voice & Song Transcription (WER ↓)</summary>
1018
+
1019
+ <table>
1020
+ <thead>
1021
+ <tr>
1022
+ <th style="text-align: left;"></th>
1023
+ <th style="text-align: center;">GPT-4o<br>-Transcribe</th>
1024
+ <th style="text-align: center;">Gemini-2.5<br>-Pro</th>
1025
+ <th style="text-align: center;">Doubao-ASR<br>-1.0</th>
1026
+ <th style="text-align: center;">Whisper<br>-large-v3</th>
1027
+ <th style="text-align: center;">Fun-ASR-MLT<br>-Nano</th>
1028
+ <th style="text-align: center;">Qwen3-ASR<br>-1.7B</th>
1029
+ </tr>
1030
+ </thead>
1031
+ <tbody>
1032
+ <tr>
1033
+ <td colspan="7" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">Singing</td>
1034
+ </tr>
1035
+ <tr>
1036
+ <td style="text-align: left;">M4Singer</td>
1037
+ <td style="text-align: center;">16.77</td>
1038
+ <td style="text-align: center;">20.88</td>
1039
+ <td style="text-align: center;">7.88</td>
1040
+ <td style="text-align: center;">13.58</td>
1041
+ <td style="text-align: center;">7.29</td>
1042
+ <td style="text-align: center;"><strong>5.98</strong></td>
1043
+ </tr>
1044
+ <tr>
1045
+ <td style="text-align: left;">MIR-1k-vocal</td>
1046
+ <td style="text-align: center;">11.87</td>
1047
+ <td style="text-align: center;">9.85</td>
1048
+ <td style="text-align: center;">6.56</td>
1049
+ <td style="text-align: center;">11.71</td>
1050
+ <td style="text-align: center;">8.17</td>
1051
+ <td style="text-align: center;"><strong>6.25</strong></td>
1052
+ </tr>
1053
+ <tr>
1054
+ <td style="text-align: left;">Opencpop</td>
1055
+ <td style="text-align: center;">7.93</td>
1056
+ <td style="text-align: center;">6.49</td>
1057
+ <td style="text-align: center;">3.80</td>
1058
+ <td style="text-align: center;">9.52</td>
1059
+ <td style="text-align: center;"><strong>2.98</strong></td>
1060
+ <td style="text-align: center;">3.08</td>
1061
+ </tr>
1062
+ <tr>
1063
+ <td style="text-align: left;">Popcs</td>
1064
+ <td style="text-align: center;">32.84</td>
1065
+ <td style="text-align: center;">15.13</td>
1066
+ <td style="text-align: center;">8.97</td>
1067
+ <td style="text-align: center;">13.77</td>
1068
+ <td style="text-align: center;">9.42</td>
1069
+ <td style="text-align: center;"><strong>8.52</strong></td>
1070
+ </tr>
1071
+ <tr>
1072
+ <td colspan="7" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">Songs with BGM</td>
1073
+ </tr>
1074
+ <tr>
1075
+ <td style="text-align: left;">EntireSongs-en</td>
1076
+ <td style="text-align: center;">30.71</td>
1077
+ <td style="text-align: center;"><strong>12.18</strong></td>
1078
+ <td style="text-align: center;">33.51</td>
1079
+ <td style="text-align: center;">N/A</td>
1080
+ <td style="text-align: center;">N/A</td>
1081
+ <td style="text-align: center;">14.60</td>
1082
+ </tr>
1083
+ <tr>
1084
+ <td style="text-align: left;">EntireSongs-zh</td>
1085
+ <td style="text-align: center;">34.86</td>
1086
+ <td style="text-align: center;">18.68</td>
1087
+ <td style="text-align: center;">23.99</td>
1088
+ <td style="text-align: center;">N/A</td>
1089
+ <td style="text-align: center;">N/A</td>
1090
+ <td style="text-align: center;"><strong>13.91</strong></td>
1091
+ </tr>
1092
+ </tbody>
1093
+ </table>
1094
+
1095
+ </details>
1096
+
1097
+ <details>
1098
+ <summary>ASR Inference Mode Performance (WER ↓)</summary>
1099
+
1100
+ <table>
1101
+ <thead>
1102
+ <tr>
1103
+ <th style="text-align: left;">Model</th>
1104
+ <th style="text-align: left;">Infer. Mode</th>
1105
+ <th style="text-align: center;">Librispeech</th>
1106
+ <th style="text-align: center;">Fleurs-en</th>
1107
+ <th style="text-align: center;">Fleurs-zh</th>
1108
+ <th style="text-align: center;">Avg.</th>
1109
+ </tr>
1110
+ </thead>
1111
+ <tbody>
1112
+ <tr>
1113
+ <td rowspan="2" style="text-align: left; vertical-align: middle;">Qwen3-ASR-1.7B</td>
1114
+ <td style="text-align: left;">Offline</td>
1115
+ <td style="text-align: center;">1.63 | 3.38</td>
1116
+ <td style="text-align: center;">3.35</td>
1117
+ <td style="text-align: center;">2.41</td>
1118
+ <td style="text-align: center;">2.69</td>
1119
+ </tr>
1120
+ <tr>
1121
+ <td style="text-align: left;">Streaming</td>
1122
+ <td style="text-align: center;">1.95 | 4.51</td>
1123
+ <td style="text-align: center;">4.02</td>
1124
+ <td style="text-align: center;">2.84</td>
1125
+ <td style="text-align: center;">3.33</td>
1126
+ </tr>
1127
+ <tr style="border-top: 1px solid #ddd;">
1128
+ <td rowspan="2" style="text-align: left; vertical-align: middle;">Qwen3-ASR-0.6B</td>
1129
+ <td style="text-align: left;">Offline</td>
1130
+ <td style="text-align: center;">2.11 | 4.55</td>
1131
+ <td style="text-align: center;">4.39</td>
1132
+ <td style="text-align: center;">2.88</td>
1133
+ <td style="text-align: center;">3.48</td>
1134
+ </tr>
1135
+ <tr>
1136
+ <td style="text-align: left;">Streaming</td>
1137
+ <td style="text-align: center;">2.54 | 6.27</td>
1138
+ <td style="text-align: center;">5.38</td>
1139
+ <td style="text-align: center;">3.40</td>
1140
+ <td style="text-align: center;">4.40</td>
1141
+ </tr>
1142
+ </tbody>
1143
+ </table>
1144
+
1145
+ </details>
1146
+
1147
+ <details>
1148
+ <summary>Forced Alignment Benchmarks (AAS ms ↓)</summary>
1149
+
1150
+ <table>
1151
+ <thead>
1152
+ <tr>
1153
+ <th style="text-align: left;"></th>
1154
+ <th style="text-align: center;">Monotonic-Aligner</th>
1155
+ <th style="text-align: center;">NFA</th>
1156
+ <th style="text-align: center;">WhisperX</th>
1157
+ <th style="text-align: center;">Qwen3-ForcedAligner-0.6B</th>
1158
+ </tr>
1159
+ </thead>
1160
+ <tbody>
1161
+ <tr>
1162
+ <td colspan="5" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">MFA-Labeled Raw</td>
1163
+ </tr>
1164
+ <tr>
1165
+ <td style="text-align: left;">Chinese</td>
1166
+ <td style="text-align: center;">161.1</td>
1167
+ <td style="text-align: center;">109.8</td>
1168
+ <td style="text-align: center;">-</td>
1169
+ <td style="text-align: center;"><strong>33.1</strong></td>
1170
+ </tr>
1171
+ <tr>
1172
+ <td style="text-align: left;">English</td>
1173
+ <td style="text-align: center;">-</td>
1174
+ <td style="text-align: center;">107.5</td>
1175
+ <td style="text-align: center;">92.1</td>
1176
+ <td style="text-align: center;"><strong>37.5</strong></td>
1177
+ </tr>
1178
+ <tr>
1179
+ <td style="text-align: left;">French</td>
1180
+ <td style="text-align: center;">-</td>
1181
+ <td style="text-align: center;">100.7</td>
1182
+ <td style="text-align: center;">145.3</td>
1183
+ <td style="text-align: center;"><strong>41.7</strong></td>
1184
+ </tr>
1185
+ <tr>
1186
+ <td style="text-align: left;">German</td>
1187
+ <td style="text-align: center;">-</td>
1188
+ <td style="text-align: center;">122.7</td>
1189
+ <td style="text-align: center;">165.1</td>
1190
+ <td style="text-align: center;"><strong>46.5</strong></td>
1191
+ </tr>
1192
+ <tr>
1193
+ <td style="text-align: left;">Italian</td>
1194
+ <td style="text-align: center;">-</td>
1195
+ <td style="text-align: center;">142.7</td>
1196
+ <td style="text-align: center;">155.5</td>
1197
+ <td style="text-align: center;"><strong>75.5</strong></td>
1198
+ </tr>
1199
+ <tr>
1200
+ <td style="text-align: left;">Japanese</td>
1201
+ <td style="text-align: center;">-</td>
1202
+ <td style="text-align: center;">-</td>
1203
+ <td style="text-align: center;">-</td>
1204
+ <td style="text-align: center;"><strong>42.2</strong></td>
1205
+ </tr>
1206
+ <tr>
1207
+ <td style="text-align: left;">Korean</td>
1208
+ <td style="text-align: center;">-</td>
1209
+ <td style="text-align: center;">-</td>
1210
+ <td style="text-align: center;">-</td>
1211
+ <td style="text-align: center;"><strong>37.2</strong></td>
1212
+ </tr>
1213
+ <tr>
1214
+ <td style="text-align: left;">Portuguese</td>
1215
+ <td style="text-align: center;">-</td>
1216
+ <td style="text-align: center;">-</td>
1217
+ <td style="text-align: center;">-</td>
1218
+ <td style="text-align: center;"><strong>38.4</strong></td>
1219
+ </tr>
1220
+ <tr>
1221
+ <td style="text-align: left;">Russian</td>
1222
+ <td style="text-align: center;">-</td>
1223
+ <td style="text-align: center;">200.7</td>
1224
+ <td style="text-align: center;">-</td>
1225
+ <td style="text-align: center;"><strong>40.2</strong></td>
1226
+ </tr>
1227
+ <tr>
1228
+ <td style="text-align: left;">Spanish</td>
1229
+ <td style="text-align: center;">-</td>
1230
+ <td style="text-align: center;">124.7</td>
1231
+ <td style="text-align: center;">108.0</td>
1232
+ <td style="text-align: center;"><strong>36.8</strong></td>
1233
+ </tr>
1234
+ <tr>
1235
+ <td style="text-align: left;"><em>Avg.</em></td>
1236
+ <td style="text-align: center;">161.1</td>
1237
+ <td style="text-align: center;">129.8</td>
1238
+ <td style="text-align: center;">133.2</td>
1239
+ <td style="text-align: center;"><strong>42.9</strong></td>
1240
+ </tr>
1241
+ <tr>
1242
+ <td colspan="5" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">MFA-Labeled Concat-300s</td>
1243
+ </tr>
1244
+ <tr>
1245
+ <td style="text-align: left;">Chinese</td>
1246
+ <td style="text-align: center;">1742.4</td>
1247
+ <td style="text-align: center;">235.0</td>
1248
+ <td style="text-align: center;">-</td>
1249
+ <td style="text-align: center;"><strong>36.5</strong></td>
1250
+ </tr>
1251
+ <tr>
1252
+ <td style="text-align: left;">English</td>
1253
+ <td style="text-align: center;">-</td>
1254
+ <td style="text-align: center;">226.7</td>
1255
+ <td style="text-align: center;">227.2</td>
1256
+ <td style="text-align: center;"><strong>58.6</strong></td>
1257
+ </tr>
1258
+ <tr>
1259
+ <td style="text-align: left;">French</td>
1260
+ <td style="text-align: center;">-</td>
1261
+ <td style="text-align: center;">230.6</td>
1262
+ <td style="text-align: center;">2052.2</td>
1263
+ <td style="text-align: center;"><strong>53.4</strong></td>
1264
+ </tr>
1265
+ <tr>
1266
+ <td style="text-align: left;">German</td>
1267
+ <td style="text-align: center;">-</td>
1268
+ <td style="text-align: center;">220.3</td>
1269
+ <td style="text-align: center;">993.4</td>
1270
+ <td style="text-align: center;"><strong>62.4</strong></td>
1271
+ </tr>
1272
+ <tr>
1273
+ <td style="text-align: left;">Italian</td>
1274
+ <td style="text-align: center;">-</td>
1275
+ <td style="text-align: center;">290.5</td>
1276
+ <td style="text-align: center;">5719.4</td>
1277
+ <td style="text-align: center;"><strong>81.6</strong></td>
1278
+ </tr>
1279
+ <tr>
1280
+ <td style="text-align: left;">Japanese</td>
1281
+ <td style="text-align: center;">-</td>
1282
+ <td style="text-align: center;">-</td>
1283
+ <td style="text-align: center;">-</td>
1284
+ <td style="text-align: center;"><strong>81.3</strong></td>
1285
+ </tr>
1286
+ <tr>
1287
+ <td style="text-align: left;">Korean</td>
1288
+ <td style="text-align: center;">-</td>
1289
+ <td style="text-align: center;">-</td>
1290
+ <td style="text-align: center;">-</td>
1291
+ <td style="text-align: center;"><strong>42.2</strong></td>
1292
+ </tr>
1293
+ <tr>
1294
+ <td style="text-align: left;">Portuguese</td>
1295
+ <td style="text-align: center;">-</td>
1296
+ <td style="text-align: center;">-</td>
1297
+ <td style="text-align: center;">-</td>
1298
+ <td style="text-align: center;"><strong>50.0</strong></td>
1299
+ </tr>
1300
+ <tr>
1301
+ <td style="text-align: left;">Russian</td>
1302
+ <td style="text-align: center;">-</td>
1303
+ <td style="text-align: center;">283.3</td>
1304
+ <td style="text-align: center;">-</td>
1305
+ <td style="text-align: center;"><strong>43.0</strong></td>
1306
+ </tr>
1307
+ <tr>
1308
+ <td style="text-align: left;">Spanish</td>
1309
+ <td style="text-align: center;">-</td>
1310
+ <td style="text-align: center;">240.2</td>
1311
+ <td style="text-align: center;">4549.9</td>
1312
+ <td style="text-align: center;"><strong>39.6</strong></td>
1313
+ </tr>
1314
+ <tr>
1315
+ <td style="text-align: left;">Cross-lingual</td>
1316
+ <td style="text-align: center;">-</td>
1317
+ <td style="text-align: center;">-</td>
1318
+ <td style="text-align: center;">-</td>
1319
+ <td style="text-align: center;"><strong>34.2</strong></td>
1320
+ </tr>
1321
+ <tr>
1322
+ <td style="text-align: left;"><em>Avg.</em></td>
1323
+ <td style="text-align: center;">1742.4</td>
1324
+ <td style="text-align: center;">246.7</td>
1325
+ <td style="text-align: center;">2708.4</td>
1326
+ <td style="text-align: center;"><strong>52.9</strong></td>
1327
+ </tr>
1328
+ <tr>
1329
+ <td colspan="5" style="text-align: left; font-style: italic; border-top: 1px solid #ddd; border-bottom: 1px solid #ddd;">Human-Labeled</td>
1330
+ </tr>
1331
+ <tr>
1332
+ <td style="text-align: left;">Raw</td>
1333
+ <td style="text-align: center;">49.9</td>
1334
+ <td style="text-align: center;">88.6</td>
1335
+ <td style="text-align: center;">-</td>
1336
+ <td style="text-align: center;"><strong>27.8</strong></td>
1337
+ </tr>
1338
+ <tr>
1339
+ <td style="text-align: left;">Raw-Noisy</td>
1340
+ <td style="text-align: center;">53.3</td>
1341
+ <td style="text-align: center;">89.5</td>
1342
+ <td style="text-align: center;">-</td>
1343
+ <td style="text-align: center;"><strong>41.8</strong></td>
1344
+ </tr>
1345
+ <tr>
1346
+ <td style="text-align: left;">Concat-60s</td>
1347
+ <td style="text-align: center;">51.1</td>
1348
+ <td style="text-align: center;">86.7</td>
1349
+ <td style="text-align: center;">-</td>
1350
+ <td style="text-align: center;"><strong>25.3</strong></td>
1351
+ </tr>
1352
+ <tr>
1353
+ <td style="text-align: left;">Concat-300s</td>
1354
+ <td style="text-align: center;">410.8</td>
1355
+ <td style="text-align: center;">140.0</td>
1356
+ <td style="text-align: center;">-</td>
1357
+ <td style="text-align: center;"><strong>24.8</strong></td>
1358
+ </tr>
1359
+ <tr>
1360
+ <td style="text-align: left;">Concat-Cross-lingual</td>
1361
+ <td style="text-align: center;">-</td>
1362
+ <td style="text-align: center;">-</td>
1363
+ <td style="text-align: center;">-</td>
1364
+ <td style="text-align: center;"><strong>42.5</strong></td>
1365
+ </tr>
1366
+ <tr>
1367
+ <td style="text-align: left;"><em>Avg.</em></td>
1368
+ <td style="text-align: center;">141.3</td>
1369
+ <td style="text-align: center;">101.2</td>
1370
+ <td style="text-align: center;">-</td>
1371
+ <td style="text-align: center;"><strong>32.4</strong></td>
1372
+ </tr>
1373
+ </tbody>
1374
+ </table>
1375
+
1376
+ </details>
1377
+
1378
+
1379
+ ## Citation
1380
+
1381
+ If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: :)
1382
+
1383
+ ```BibTeX
1384
+ @article{Qwen3-ASR,
1385
+ title={Qwen3-ASR Technical Report},
1386
+ author={Xian Shi, Xiong Wang, Zhifang Guo, Yongqi Wang, Pei Zhang, Xinyu Zhang, Zishan Guo, Hongkun Hao, Yu Xi, Baosong Yang, Jin Xu, Jingren Zhou, Junyang Lin},
1387
+ journal={arXiv preprint arXiv:2601.21337},
1388
+ year={2026}
1389
+ }
1390
+ ```
1391
+
1392
+
1393
+ <br>
chat_template.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"chat_template": "{%- set ns = namespace(system_text=\"\") -%}\n{%- for m in messages -%}\n {%- if m.role == 'system' -%}\n {%- if m.content is string -%}\n {%- set ns.system_text = ns.system_text + m.content -%}\n {%- else -%}\n {%- for c in m.content -%}\n {%- if c.type == 'text' and (c.text is defined) -%}\n {%- set ns.system_text = ns.system_text + c.text -%}\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n {%- endif -%}\n{%- endfor -%}\n\n{%- set ns2 = namespace(audio_tokens=\"\") -%}\n{%- for m in messages -%}\n {%- if m.content is not string -%}\n {%- for c in m.content -%}\n {%- if c.type == 'audio' or ('audio' in c) or ('audio_url' in c) -%}\n {%- set ns2.audio_tokens = ns2.audio_tokens + \"<|audio_start|><|audio_pad|><|audio_end|>\" -%}\n {%- endif -%}\n {%- endfor -%}\n {%- endif -%}\n{%- endfor -%}\n\n{{- '<|im_start|>system\\n' + (ns.system_text if ns.system_text is string else '') + '<|im_end|>\\n' -}}\n{{- '<|im_start|>user\\n' + ns2.audio_tokens + '<|im_end|>\\n' -}}\n{%- if add_generation_prompt -%}\n{{- '<|im_start|>assistant\\n' -}}\n{%- endif -%}"}
config.json ADDED
@@ -0,0 +1,221 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "Qwen3ASRForConditionalGeneration"
4
+ ],
5
+ "model_type": "qwen3_asr",
6
+ "support_languages": [
7
+ "Chinese",
8
+ "English",
9
+ "Cantonese",
10
+ "Arabic",
11
+ "German",
12
+ "French",
13
+ "Spanish",
14
+ "Portuguese",
15
+ "Indonesian",
16
+ "Italian",
17
+ "Korean",
18
+ "Russian",
19
+ "Thai",
20
+ "Vietnamese",
21
+ "Japanese",
22
+ "Turkish",
23
+ "Hindi",
24
+ "Malay",
25
+ "Dutch",
26
+ "Swedish",
27
+ "Danish",
28
+ "Finnish",
29
+ "Polish",
30
+ "Czech",
31
+ "Filipino",
32
+ "Persian",
33
+ "Greek",
34
+ "Romanian",
35
+ "Hungarian",
36
+ "Macedonian"
37
+ ],
38
+ "thinker_config": {
39
+ "model_type": "qwen3_asr",
40
+ "architectures": [
41
+ "Qwen3ASRForConditionalGeneration"
42
+ ],
43
+ "audio_config": {
44
+ "_name_or_path": "",
45
+ "activation_dropout": 0,
46
+ "activation_function": "gelu",
47
+ "add_cross_attention": false,
48
+ "architectures": null,
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+ "attention_dropout": 0,
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+ "bad_words_ids": null,
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+ "d_model": 896,
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+ "decoder_start_token_id": null,
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+ "diversity_penalty": 0.0,
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+ "do_sample": false,
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+ "downsample_hidden_size": 480,
61
+ "dropout": 0,
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+ "dtype": null,
63
+ "early_stopping": false,
64
+ "encoder_attention_heads": 14,
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+ "encoder_ffn_dim": 3584,
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+ "encoder_layers": 18,
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+ "encoder_no_repeat_ngram_size": 0,
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+ "exponential_decay_length_penalty": null,
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+ "id2label": {
74
+ "0": "LABEL_0",
75
+ "1": "LABEL_1"
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+ },
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+ "initializer_range": 0.02,
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+ "is_decoder": false,
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+ "label2id": {
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+ "LABEL_1": 1
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+ },
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+ "length_penalty": 1.0,
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+ "max_length": 20,
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+ "max_source_positions": 1500,
87
+ "min_length": 0,
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+ "model_type": "qwen3_asr_audio_encoder",
89
+ "n_window": 50,
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+ "n_window_infer": 800,
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+ "no_repeat_ngram_size": 0,
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+ "num_beam_groups": 1,
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+ },
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+ "audio_end_token_id": 151670,
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+ "audio_start_token_id": 151669,
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+ "audio_token_id": 151676,
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+ "dtype": "bfloat16",
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+ "hidden_act": "silu",
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+ "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1"
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+ },
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+ "initializer_range": 0.02,
159
+ "intermediate_size": 3072,
160
+ "is_decoder": false,
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163
+ "LABEL_0": 0,
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+ "LABEL_1": 1
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+ },
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+ "length_penalty": 1.0,
167
+ "max_length": 20,
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+ "max_position_embeddings": 65536,
169
+ "min_length": 0,
170
+ "model_type": "qwen3",
171
+ "no_repeat_ngram_size": 0,
172
+ "num_attention_heads": 16,
173
+ "num_beam_groups": 1,
174
+ "num_beams": 1,
175
+ "num_hidden_layers": 28,
176
+ "num_key_value_heads": 8,
177
+ "num_return_sequences": 1,
178
+ "output_attentions": false,
179
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+ "output_scores": false,
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+ "pad_token_id": null,
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+ "problem_type": null,
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+ "pruned_heads": {},
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+ "remove_invalid_values": false,
186
+ "repetition_penalty": 1.0,
187
+ "return_dict": true,
188
+ "return_dict_in_generate": false,
189
+ "rms_norm_eps": 1e-06,
190
+ "rope_scaling": {
191
+ "interleaved": true,
192
+ "mrope_interleaved": true,
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+ "mrope_section": [
194
+ 24,
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+ 20,
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+ 20
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+ ],
198
+ "rope_type": "default",
199
+ "type": "default"
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+ },
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+ "rope_theta": 1000000,
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+ "temperature": 1.0,
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+ "tie_encoder_decoder": false,
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+ "tie_word_embeddings": true,
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+ "tokenizer_class": null,
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+ "top_k": 50,
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+ "top_p": 1.0,
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+ "torchscript": false,
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+ "typical_p": 1.0,
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+ "use_bfloat16": false,
215
+ "use_cache": true,
216
+ "vocab_size": 151936
217
+ }
218
+ },
219
+ "transformers_version": "4.57.6"
220
+ }
221
+
generation_config.json ADDED
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+ "pad_token_id": 151643,
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+ "do_sample": false,
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+ "temperature": 0.000001
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+ }
merges.txt ADDED
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+ "dither": 0.0,
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vocab.json ADDED
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