From 811c516932fd91d17ada8e802a3b2e9ae028be66 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 31 十月 2024 18:39:34 +0800
Subject: [PATCH] docs: 更新教程文档链接
---
docs/tutorial/README.md | 61 ++++++++++++++++++++++++++++++
1 files changed, 60 insertions(+), 1 deletions(-)
diff --git a/docs/tutorial/README.md b/docs/tutorial/README.md
index f87d5fa..1180c82 100644
--- a/docs/tutorial/README.md
+++ b/docs/tutorial/README.md
@@ -7,6 +7,7 @@
<a href="#Inference"> Model Inference </a>
锝�<a href="#Training"> Model Training and Testing </a>
锝�<a href="#Export"> Model Export and Testing </a>
+锝�<a href="#new-model-registration-tutorial"> New Model Registration Tutorial </a>
</h4>
</div>
@@ -95,7 +96,9 @@
When you input long audio and encounter Out Of Memory (OOM) issues, since memory usage tends to increase quadratically with audio length, consider the following three scenarios:
a) At the beginning of inference, memory usage primarily depends on `batch_size_s`. Appropriately reducing this value can decrease memory usage.
+
b) During the middle of inference, when encountering long audio segments cut by VAD and the total token count is less than `batch_size_s`, yet still facing OOM, you can appropriately reduce `batch_size_threshold_s`. If the threshold is exceeded, the batch size is forced to 1.
+
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 (Streaming)
@@ -421,4 +424,60 @@
print(result)
```
-More examples ref to [demo](https://github.com/alibaba-damo-academy/FunASR/tree/main/runtime/python/onnxruntime)
\ No newline at end of file
+More examples ref to [demo](https://github.com/alibaba-damo-academy/FunASR/tree/main/runtime/python/onnxruntime)
+
+
+<a name="new-model-registration-tutorial"></a>
+## New Model Registration Tutorial
+
+### Viewing the Registry
+
+```plaintext
+from funasr.register import tables
+
+tables.print()
+```
+
+Supports viewing the registry of a specified type: `tables.print("model")`
+
+### Registering Models
+
+```python
+from funasr.register import tables
+
+@tables.register("model_classes", "SenseVoiceSmall")
+class SenseVoiceSmall(nn.Module):
+ def __init__(*args, **kwargs):
+ ...
+
+ def forward(
+ self,
+ **kwargs,
+ ):
+
+ def inference(
+ self,
+ data_in,
+ data_lengths=None,
+ key: list = None,
+ tokenizer=None,
+ frontend=None,
+ **kwargs,
+ ):
+ ...
+
+```
+
+Add `@tables.register("model_classes","SenseVoiceSmall")` before the class name that needs to be registered to complete the registration. The class needs to implement the methods: __init__, forward, and inference.
+
+Complete code: [https://github.com/modelscope/FunASR/blob/main/funasr/models/sense_voice/model.py#L443](https://github.com/modelscope/FunASR/blob/main/funasr/models/sense_voice/model.py#L443)
+
+After registration, specify the newly registered model in config.yaml to define the model
+
+```python
+model: SenseVoiceSmall
+model_conf:
+ ...
+```
+
+[More detailed tutorial documents](https://github.com/modelscope/FunASR/blob/main/docs/tutorial/Tables.md)
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