| | |
| | | ``` |
| | | 注:`model_hub`:表示模型仓库,`ms`为选择modelscope下载,`hf`为选择huggingface下载。 |
| | | |
| | | ### 实时语音识别 |
| | | ```python |
| | | from funasr import infer |
| | | [//]: # (### 实时语音识别) |
| | | |
| | | p = infer(model="paraformer-zh-streaming", model_hub="ms") |
| | | [//]: # (```python) |
| | | |
| | | chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms |
| | | param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size, "encoder_chunk_look_back": 4, "decoder_chunk_look_back": 1} |
| | | [//]: # (from funasr import infer) |
| | | |
| | | import torchaudio |
| | | speech = torchaudio.load("asr_example_zh.wav")[0][0] |
| | | speech_length = speech.shape[0] |
| | | [//]: # () |
| | | [//]: # (p = infer(model="paraformer-zh-streaming", model_hub="ms")) |
| | | |
| | | stride_size = chunk_size[1] * 960 |
| | | sample_offset = 0 |
| | | for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)): |
| | | param_dict["is_final"] = True if sample_offset + stride_size >= speech_length - 1 else False |
| | | input = speech[sample_offset: sample_offset + stride_size] |
| | | rec_result = p(input=input, param_dict=param_dict) |
| | | print(rec_result) |
| | | ``` |
| | | 注:`chunk_size`为流式延时配置,`[0,10,5]`表示上屏实时出字粒度为`10*60=600ms`,未来信息为`5*60=300ms`。每次推理输入为`600ms`(采样点数为`16000*0.6=960`),输出为对应文字,最后一个语音片段输入需要设置`is_final=True`来强制输出最后一个字。 |
| | | [//]: # () |
| | | [//]: # (chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms) |
| | | |
| | | 更多详细用法([新人文档](https://alibaba-damo-academy.github.io/FunASR/en/funasr/quick_start_zh.html)) |
| | | [//]: # (param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size, "encoder_chunk_look_back": 4, "decoder_chunk_look_back": 1}) |
| | | |
| | | [//]: # () |
| | | [//]: # (import torchaudio) |
| | | |
| | | [//]: # (speech = torchaudio.load("asr_example_zh.wav")[0][0]) |
| | | |
| | | [//]: # (speech_length = speech.shape[0]) |
| | | |
| | | [//]: # () |
| | | [//]: # (stride_size = chunk_size[1] * 960) |
| | | |
| | | [//]: # (sample_offset = 0) |
| | | |
| | | [//]: # (for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):) |
| | | |
| | | [//]: # ( param_dict["is_final"] = True if sample_offset + stride_size >= speech_length - 1 else False) |
| | | |
| | | [//]: # ( input = speech[sample_offset: sample_offset + stride_size]) |
| | | |
| | | [//]: # ( rec_result = p(input=input, param_dict=param_dict)) |
| | | |
| | | [//]: # ( print(rec_result)) |
| | | |
| | | [//]: # (```) |
| | | |
| | | [//]: # (注:`chunk_size`为流式延时配置,`[0,10,5]`表示上屏实时出字粒度为`10*60=600ms`,未来信息为`5*60=300ms`。每次推理输入为`600ms`(采样点数为`16000*0.6=960`),输出为对应文字,最后一个语音片段输入需要设置`is_final=True`来强制输出最后一个字。) |
| | | |
| | | [//]: # () |
| | | [//]: # (更多详细用法([新人文档](https://alibaba-damo-academy.github.io/FunASR/en/funasr/quick_start_zh.html))) |
| | | |
| | | |
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