From a76f15c78527edf47a68dd67e2c4d894bc252398 Mon Sep 17 00:00:00 2001
From: Djraemon <154407769+Djraemon@users.noreply.github.com>
Date: 星期一, 21 十月 2024 13:30:45 +0800
Subject: [PATCH] Fix audio format (#2159)
---
docs/tutorial/README.md | 6 ++++--
1 files changed, 4 insertions(+), 2 deletions(-)
diff --git a/docs/tutorial/README.md b/docs/tutorial/README.md
index ef1b208..9b68e73 100644
--- a/docs/tutorial/README.md
+++ b/docs/tutorial/README.md
@@ -95,7 +95,9 @@
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)
@@ -130,7 +132,7 @@
from funasr import AutoModel
model = AutoModel(model="fsmn-vad")
-wav_file = f"{model.model_path}/example/asr_example.wav"
+wav_file = f"{model.model_path}/example/vad_example.wav"
res = model.generate(input=wav_file)
print(res)
```
@@ -421,4 +423,4 @@
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)
--
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