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fasr-asr-qwen3

内置 Qwen3-ASR 推理(Transformers / vLLM)的语音识别模型插件,为 fasr 提供无时间戳 ASR 能力。

安装

pip install fasr-asr-qwen3

注册模型

注册名 默认 checkpoint 说明
qwen3_0_6b Qwen3_06BForASR Qwen/Qwen3-ASR-0.6B 离线 ASR,当前不返回时间戳
qwen3_1_7b Qwen3_17BForASR Qwen/Qwen3-ASR-1.7B 离线 ASR,当前不返回时间戳
stream_qwen3_0_6b Qwen3_06BForStreamASR Qwen/Qwen3-ASR-0.6B 流式 ASR(vLLM 后端),每 chunk_size_ms 重新解码一次
stream_qwen3_1_7b Qwen3_17BForStreamASR Qwen/Qwen3-ASR-1.7B 流式 ASR(vLLM 后端),每 chunk_size_ms 重新解码一次

使用方式

from fasr import AudioPipeline

pipeline = (
    AudioPipeline()
    .add_pipe("detector", model="fsmn")
    .add_pipe("recognizer", model="qwen3_1.7b")  # 或 qwen3_0.6b
    .add_pipe("sentencizer", model="ct_transformer")
)

单独使用模型

模型实例化时会自动执行 download_checkpoint() + load_checkpoint()

from fasr.config import registry

model = registry.asr_models.get("qwen3_1.7b")(gpu_memory_utilization=0.6)
# or model.load_checkpoint("/path/to/custom/qwen3")

运行期 / 会话参数

参数 类型 默认值 说明
checkpoint str | None 子类各自默认 远程 repo_id;非空时实例化会自动下载到 cache_dir
cache_dir str | Path | None None 缓存目录,None 使用 fasr.utils.get_cache_dir()
endpoint Literal["modelscope", "huggingface", "hf-mirror"] "modelscope" 下载端点
max_new_tokens int 4096 最大生成 token 数
max_inference_batch_size int -1 vLLM 推理批次上限,-1 不限制
gpu_memory_utilization float 0.8 vLLM 可占用的 GPU 显存比例,(0, 1]
max_model_len int | None None vLLM max_model_lenNone 回退为 max_new_tokens * 2

流式使用(StreamASR)

stream_qwen3_* 实现了 ASRModel.push_chunk,接在流式 VAD 之后即可:

from fasr.config import registry

model = registry.stream_asr_models.get("stream_qwen3_0_6b")(
    chunk_size_ms=2000,
    language="zh",          # 可选:强制语种
)
for chunk in audio_chunk_stream:
    span = model.push_chunk(chunk)
    if span is not None:
        print(span.text, flush=True)

注意:Qwen3-ASR 流式本质是“累计音频重解码”,每个 chunk 返回的是 当前累计结果(AudioSpan.raw_text)。后续 chunk 可能会改写前文, 所以上层消费应采用“覆盖显示最新文本”,而不是拼接增量 delta。

输出说明

  • 当前模型不返回词级/字级时间戳。
  • 离线模式把整段识别文本写入 span.raw_textspan.text 即可访问); 流式模式每个 chunk 都返回当前累计文本(AudioSpan(raw_text=...)), 最后一个 chunk 的返回带 is_last=True

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