asr / setup_env.sh
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#!/bin/bash
# ============================================================================
# ASR 诗词纠错训练环境配置脚本
# ============================================================================
set -e
echo "=========================================="
echo "ASR 诗词纠错训练环境配置"
echo "=========================================="
# ============================================================================
# 1. 创建 conda 环境 (可选,如果需要新环境)
# ============================================================================
# 如果需要创建新环境,取消下面的注释:
# conda create -n asr_correct python=3.10 -y
# conda activate asr_correct
# ============================================================================
# 2. 安装核心依赖
# ============================================================================
echo "安装核心依赖..."
# PyTorch (根据 CUDA 版本选择)
# H200 建议使用 CUDA 12.x
pip install torch==2.1.2 --index-url https://download.pytorch.org/whl/cu121
# Transformers 生态
pip install transformers==4.47.1
pip install accelerate==1.5.2
pip install datasets==3.2.0
pip install tokenizers==0.21.1
# PEFT (LoRA 训练)
pip install peft==0.15.0
# 其他必需
pip install loguru==0.7.3
pip install tqdm==4.67.1
pip install tensorboardX==2.6.2.2
pip install safetensors==0.5.3
pip install sentencepiece==0.2.0
# 拼音处理 (数据生成用)
pip install pypinyin==0.53.0
# ============================================================================
# 3. 可选依赖
# ============================================================================
echo "安装可选依赖..."
# vLLM (推理加速,可选)
# pip install vllm==0.3.3
# bitsandbytes (QLoRA 量化,可选)
# pip install bitsandbytes
# ============================================================================
# 4. 验证安装
# ============================================================================
echo ""
echo "验证安装..."
python -c "
import torch
print(f'PyTorch: {torch.__version__}')
print(f'CUDA available: {torch.cuda.is_available()}')
if torch.cuda.is_available():
print(f'CUDA version: {torch.version.cuda}')
print(f'GPU: {torch.cuda.get_device_name(0)}')
print(f'GPU Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB')
import transformers
print(f'Transformers: {transformers.__version__}')
import peft
print(f'PEFT: {peft.__version__}')
import accelerate
print(f'Accelerate: {accelerate.__version__}')
print('✅ 所有依赖安装成功!')
"
echo ""
echo "=========================================="
echo "环境配置完成!"
echo "=========================================="