From b75d1e89bb2f513a79bb07e9100ba1cd2bbcf40c Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期日, 09 六月 2024 00:32:57 +0800
Subject: [PATCH] fix bug
---
examples/README.md | 12 ++++++------
1 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/examples/README.md b/examples/README.md
index 20102cc..0191a2d 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -130,7 +130,7 @@
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)
```
@@ -221,7 +221,7 @@
++train_conf.validate_interval=2000 \
++train_conf.save_checkpoint_interval=2000 \
++train_conf.keep_nbest_models=20 \
-++train_conf.avg_nbest_model=5 \
+++train_conf.avg_nbest_model=10 \
++optim_conf.lr=0.0002 \
++output_dir="${output_dir}" &> ${log_file}
```
@@ -248,10 +248,10 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
-torchrun --nnodes 1 --nproc_per_node ${gpu_num} \
+torchrun --nnodes 1 --nproc_per_node ${gpu_num} --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```
---nnodes represents the total number of participating nodes, while --nproc_per_node indicates the number of processes running on each node.
+--nnodes represents the total number of participating nodes, while --nproc_per_node indicates the number of processes running on each node. --master_port indicates the port is 12345
##### Multi-Machine Multi-GPU Training
@@ -260,7 +260,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
-torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
+torchrun --nnodes 2 --node_rank 0 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```
On the worker node (assuming the IP is 192.168.1.2), you need to ensure that the MASTER_ADDR and MASTER_PORT environment variables are set to match those of the master node, and then run the same command:
@@ -269,7 +269,7 @@
export CUDA_VISIBLE_DEVICES="0,1"
gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
-torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr=192.168.1.1 --master_port=12345 \
+torchrun --nnodes 2 --node_rank 1 --nproc_per_node ${gpu_num} --master_addr 192.168.1.1 --master_port 12345 \
../../../funasr/bin/train.py ${train_args}
```
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