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docs: fix tutorial - len() syntax, add Fun-ASR-Nano section, remove duplicates

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游雁
2026-05-20 05:51:15 +00:00
parent a4a28fea60
commit ab4f610faa
2 changed files with 27 additions and 16 deletions
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docs/tutorial/README.md
+25 -14
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@@ -101,6 +101,29 @@ b) During the middle of inference, when encountering long audio segments cut by
c) Towards the end of inference, if long audio segments cut by VAD have a total token count less than `batch_size_s` and exceed the `threshold` batch_size_threshold_s, forcing the batch size to 1 and still facing OOM, you may reduce `max_single_segment_time` to shorten the VAD audio segment length.
#### Speech Recognition (Fun-ASR-Nano)
```python
from funasr import AutoModel
model = AutoModel(
model="FunAudioLLM/Fun-ASR-Nano-2512",
trust_remote_code=True,
remote_code="./model.py",
vad_model="fsmn-vad",
vad_kwargs={"max_single_segment_time": 30000},
device="cuda:0",
hub="hf",
)
res = model.generate(input="audio.wav", cache={}, batch_size=1, language="中文")
print(res[0]["text"])
print(res[0]["timestamps"]) # character-level timestamps
```
Notes:
- Fun-ASR-Nano is trained on tens of millions of hours of data, supporting 31 languages including Chinese dialects.
- Supports hotwords: `hotwords=["keyword1", "keyword2"]`
- Supports speaker diarization: add `spk_model="cam++"` and `punc_model="ct-punc"` to get `sentence_info` with speaker labels.
- Requires: `pip install tiktoken huggingface_hub`
#### Speech Recognition (Streaming)
```python
from funasr import AutoModel
@@ -119,7 +142,7 @@ speech, sample_rate = soundfile.read(wav_file)
chunk_stride = chunk_size[1] * 960 # 600ms
cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
total_chunk_num = int((len(speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
@@ -153,7 +176,7 @@ speech, sample_rate = soundfile.read(wav_file)
chunk_stride = int(chunk_size * sample_rate / 1000)
cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
total_chunk_num = int((len(speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
@@ -252,20 +275,8 @@ Notes: Requires `transformers>=5.0.0` (install from source: `pip install git+htt
#### Speaker Verification / Diarization (ERes2NetV2)
Notes: can also be (CAM++ model). ERes2NetV2 provides improved short-duration speaker feature extraction.
#### Multi-language ASR (Qwen3-ASR)
Notes: Requires . Supports 0.6B and 1.7B model sizes.
#### Multi-language ASR (GLM-ASR)
Notes: Requires (install from source: ).
<a name="Training"></a>
## Model Training and Testing
docs/tutorial/README_zh.md
+2 -2
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@@ -116,7 +116,7 @@ speech, sample_rate = soundfile.read(wav_file)
chunk_stride = chunk_size[1] * 960 # 600ms
cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
total_chunk_num = int((len(speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1
@@ -153,7 +153,7 @@ speech, sample_rate = soundfile.read(wav_file)
chunk_stride = int(chunk_size * sample_rate / 1000)
cache = {}
total_chunk_num = int(len((speech)-1)/chunk_stride+1)
total_chunk_num = int((len(speech)-1)/chunk_stride+1)
for i in range(total_chunk_num):
speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
is_final = i == total_chunk_num - 1