hw05peft_lora_whisper-large-v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "647622af-0b68-418e-b6e6-4f22e9871434",
   "metadata": {},
   "source": [
    "## PEFT 库 LoRA 实战 - OpenAI Whisper-large-v2\r\n",
    "本教程使用 LoRA 在OpenAI Whisper-large-v2模型上实现语音识别(ASR)任务的微调训练。\r\n",
    "\r\n",
    "同时，我们还结合了int8 量化进一步降低训练过程资源开销，同时保证了精度几乎不受影响。"
   ]
  },
  {
   "attachments": {
    "d788225c-f1da-4730-ad12-4f3de7961c68.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "e47bbeb7-c086-4388-8524-7a3956f73308",
   "metadata": {},
   "source": [
    "![image.png](attachment:d788225c-f1da-4730-ad12-4f3de7961c68.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bcb924d-9cd9-4ae7-8041-62df26e29780",
   "metadata": {},
   "source": [
    "### 全局参数设置\n",
    "\n",
    "#### 数据准备\n",
    "- 下载数据集：训练、验证和评估集\n",
    "- 预处理数据：降采样、移除不必要字段等\n",
    "- 数据抽样（演示需要）\n",
    "- 应用数据集处理\n",
    "  - 使用 `Dataset.map` 进行数据处理\n",
    "  - 自定义语音数据处理器\n",
    "\n",
    "#### 模型准备\n",
    "- 加载和处理 int8 精度 Whisper-Large-v2 模型\n",
    "- LoRA Adapter 参数配置\n",
    "- 实例化 PEFT Model：`peft_model = get_peft_model(model, config)`\n",
    "\n",
    "#### 模型训练\n",
    "- 训练参数配置 `Seq2SeqTrainingArguments`\n",
    "- 实例化训练器 `Seq2SeqTrainer`\n",
    "- 训练模型\n",
    "- 保存模型\n",
    "\n",
    "#### 模型推理\n",
    "- 使用 PeftModel 加载 LoRA 微调后 Whisper 模型\n",
    "- 使用 Pipeline API 部署微调后 Whisper 实现中文语音识别任务"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff67c98-c119-4ad6-8895-b52df9690da7",
   "metadata": {},
   "source": [
    "### 全局参数设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d156d6ff-c759-4787-ba51-b89a4e759427",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name_or_path = \"openai/whisper-large-v2\"\n",
    "model_dir = \"models/whisper-large-v2-asr-int8\"\n",
    "\n",
    "language = \"Chinese (China)\"\n",
    "language_abbr = \"zh-CN\"\n",
    "task = \"transcribe\"\n",
    "dataset_name = \"mozilla-foundation/common_voice_11_0\"\n",
    "batch_size=16"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eedb154-b0a2-459e-9d95-13e0ab9b0b07",
   "metadata": {},
   "source": [
    "### 数据准备\r\n",
    "下载数据集 Common Voice\r\n",
    "Common Voice 11.0 数据集包含许多不同语言的录音，总时长达数小时。\r\n",
    "\r\n",
    "本教程以中文数据为例，展示如何使用 LoRA 在 Whisper-large-v2 上进行微调训练。\r\n",
    "\r\n",
    "首先，初始化一个DatasetDict结构，并将训练集（将训练+验证拆分为训练集）和测试集拆分好，按照中文数据集构建配置加载到内存中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d71779b2-5695-417c-84d2-b9e2fba21f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b649843bd6634202a66fafc8c707ef9b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/8.13k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d161c2540034c03986fbc8d7ae67fb9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/14.4k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7538ed63c0864381a56054eadd80814b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading extra modules:   0%|          | 0.00/3.44k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1612a1211314476bfa731c3816e4842",
       "version_major": 2,
       "version_minor": 0
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      },
      "text/plain": [
       "Generating invalidated split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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    },
    {
     "data": {
      "text/plain": [
       "{'client_id': '95368aab163e0387e4fd4991b4f2d8ccfbd4364bf656c860230501fd27dcedf087773e4695a6cf5de9c4f1d406d582283190d065cdfa36b0e2b060cffaca977e',\n",
       " 'path': '/root/.cache/huggingface/datasets/downloads/extracted/dcc5967c754d4c815fc005d6e297d84537028996cbcf6b34190517630cbc40b4/zh-CN_train_0/common_voice_zh-CN_33211332.mp3',\n",
       " 'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/dcc5967c754d4c815fc005d6e297d84537028996cbcf6b34190517630cbc40b4/zh-CN_train_0/common_voice_zh-CN_33211332.mp3',\n",
       "  'array': array([-6.82121026e-13, -2.27373675e-12, -2.27373675e-12, ...,\n",
       "          1.21667399e-05,  3.23003678e-06, -2.43066324e-07]),\n",
       "  'sampling_rate': 48000},\n",
       " 'sentence': '性喜温暖润湿气候且耐寒。',\n",
       " 'up_votes': 2,\n",
       " 'down_votes': 0,\n",
       " 'age': '',\n",
       " 'gender': '',\n",
       " 'accent': '',\n",
       " 'locale': 'zh-CN',\n",
       " 'segment': ''}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset, DatasetDict\n",
    "\n",
    "common_voice = DatasetDict()\n",
    "\n",
    "common_voice[\"train\"] = load_dataset(dataset_name, language_abbr, split=\"train\", trust_remote_code=True)\n",
    "common_voice[\"validation\"] = load_dataset(dataset_name, language_abbr, split=\"validation\", trust_remote_code=True)\n",
    "\n",
    "common_voice[\"train\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dbd3da3-75a9-42d6-9012-3425d01bbd5f",
   "metadata": {},
   "source": [
    "### 预处理训练数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e6732572-3943-44f0-b567-288f6f6910c5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/miniconda3/envs/transformers/lib/python3.11/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "2024-02-28 14:07:59.638571: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-02-28 14:08:00.124831: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-02-28 14:08:00.124915: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-02-28 14:08:00.191375: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-02-28 14:08:00.389061: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-02-28 14:08:02.100895: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "728edead62c640558b0af54ff9d03293",
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       "model_id": "cc37730075694c58869d79f41b38a5fe",
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     },
     "metadata": {},
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    },
    {
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       "model_id": "708ed4325cfa48eba2a820dda7a3e427",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "957d683e40cc4dd29c3a8c8608be8242",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "f16b70eb10d94c7bb70c3555092b3701",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "fe4878f8a59f4ae3ab70dfe240223de9",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5f3e1c2fe474a179331b82d799e087a",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoFeatureExtractor, AutoTokenizer, AutoProcessor\n",
    "\n",
    "# 从预训练模型加载特征提取器\n",
    "feature_extractor = AutoFeatureExtractor.from_pretrained(model_name_or_path)\n",
    "\n",
    "# 从预训练模型加载分词器，可以指定语言和任务以获得最适合特定需求的分词器配置\n",
    "tokenizer = AutoTokenizer.from_pretrained(\n",
    "    model_name_or_path, language=language, task=task)\n",
    "\n",
    "# 从预训练模型加载处理器，处理器通常结合了特征提取器和分词器，为特定任务提供一站式的数据预处理\n",
    "processor = AutoProcessor.from_pretrained(\n",
    "    model_name_or_path, language=language, task=task)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf31c0b3-8597-467f-b5c0-850be6692aae",
   "metadata": {},
   "source": [
    "### 移除数据集中不必要的字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82b61487-b7e9-4a36-80d5-706bd75e2af0",
   "metadata": {},
   "outputs": [],
   "source": [
    "common_voice = common_voice.remove_columns(\n",
    "    [\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"path\", \"segment\", \"up_votes\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "839c04a7-1135-42c7-836b-3f8b4b4eb7a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/dcc5967c754d4c815fc005d6e297d84537028996cbcf6b34190517630cbc40b4/zh-CN_train_0/common_voice_zh-CN_33211332.mp3',\n",
       "  'array': array([-6.82121026e-13, -2.27373675e-12, -2.27373675e-12, ...,\n",
       "          1.21667399e-05,  3.23003678e-06, -2.43066324e-07]),\n",
       "  'sampling_rate': 48000},\n",
       " 'sentence': '性喜温暖润湿气候且耐寒。'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice[\"train\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e843aa-5ce7-42b8-80b4-63fb987647ac",
   "metadata": {},
   "source": [
    "### 降采样音频数据\r\n",
    "查看common_voice 数据集介绍，你会发现其音频是以48kHz的采样率进行采样的.\r\n",
    "\r\n",
    "而Whisper模型是在16kHZ的音频输入上预训练的，因此我们需要将音频输入降采样以匹配模型预训练时使用的采样率。\r\n",
    "\r\n",
    "通过在音频列上使用cast_column方法，并将sampling_rate设置为16kHz来对音频进行降采样。\r\n",
    "\r\n",
    "下次调用时，音频输入将实时重新取样："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9dec349a-3169-44a7-bf35-735f49c88fed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import Audio\n",
    "\n",
    "common_voice = common_voice.cast_column(\"audio\", Audio(sampling_rate=16000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cbf34b7a-5409-4b4c-95c6-1b6c378fc4c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/dcc5967c754d4c815fc005d6e297d84537028996cbcf6b34190517630cbc40b4/zh-CN_train_0/common_voice_zh-CN_33211332.mp3',\n",
       "  'array': array([ 5.09317033e-11, -7.27595761e-12, -6.54836185e-11, ...,\n",
       "         -5.96661994e-06,  2.71382887e-05,  1.29687978e-05]),\n",
       "  'sampling_rate': 16000},\n",
       " 'sentence': '性喜温暖润湿气候且耐寒。'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sampling_rate 从 48KHZ 降为 16KHZ\n",
    "common_voice[\"train\"][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daa2c34f-e244-47f5-a932-3de38d1c68f8",
   "metadata": {},
   "source": [
    "#### 整合以上数据处理为一个函数\n",
    "该数据预处理函数应该包括：\n",
    "\n",
    "- 通过加载音频列将音频输入重新采样为16kHZ。\n",
    "- 使用特征提取器从音频数组计算输入特征。\n",
    "- 将句子列标记化为输入标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "963cf8af-5ace-470a-ad68-ea353d1e0fd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(batch):\n",
    "    audio = batch[\"audio\"]\n",
    "    batch[\"input_features\"] = feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
    "    batch[\"labels\"] = tokenizer(batch[\"sentence\"]).input_ids\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9420b182-ec57-4c18-aeb6-ee18b47dc7f3",
   "metadata": {},
   "source": [
    "### 数据抽样（选取1600个训练样本，800个验证样本作为homework，NVIDIA T4 显卡上耗时约30-40分钟。PS：全部样例需要训练6个小时左右）\n",
    "在 Whisper-Large-v2 上使用小规模数据进行演示训练，保持以下训练参数不变（batch_size=16，PS：设置为64时，16G显存刷爆了）。\n",
    "\n",
    "使用 1600 个样本训练，800个样本验证和评估，恰好使得1个 epoch 仅需100 steps 即可完成训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "13580570-ac81-48da-897c-dcbc5e27664a",
   "metadata": {},
   "outputs": [],
   "source": [
    "small_common_voice = DatasetDict()\n",
    "\n",
    "small_common_voice[\"train\"] = common_voice[\"train\"].shuffle(seed=16).select(range(1600))\n",
    "small_common_voice[\"validation\"] = common_voice[\"validation\"].shuffle(seed=16).select(range(800))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "11de5452-8bf3-45ab-bf3d-8bab83410d27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['audio', 'sentence'],\n",
       "        num_rows: 1600\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['audio', 'sentence'],\n",
       "        num_rows: 800\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_common_voice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68a03bf-a2ab-41ba-a69a-95fef0a44f29",
   "metadata": {},
   "source": [
    "### 如果全量训练，则使用完整数据代替抽样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cde147d5-95df-4ae5-b3b2-a39d9aaee3b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9159c080af9a4b1cbbc705ec09bf2a02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1600 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "250ec785718346659198b805bc37ebe8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/800 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 抽样数据处理\n",
    "tokenized_common_voice = small_common_voice.map(prepare_dataset)\n",
    "\n",
    "#完整数据训练，尝试开启 `num_proc=8` 参数多进程并行处理（如阻塞无法运行，则不使用此参数）\n",
    "# tokenized_common_voice = common_voice.map(prepare_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "592c9793-ce53-4db6-8c32-5e8e4f6357d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['audio', 'sentence', 'input_features', 'labels'],\n",
       "        num_rows: 1600\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['audio', 'sentence', 'input_features', 'labels'],\n",
       "        num_rows: 800\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_common_voice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad9eb80-8a17-4341-bb79-981e0b0b4447",
   "metadata": {},
   "source": [
    "### 自定义语音数据整理器\r\n",
    "定义了一个针对语音到文本（Seq2Seq）模型的自定义数据整理器类，特别适用于输入为语音特征、输出为文本序列的数据集。\r\n",
    "\r\n",
    "这个整理器（DataCollatorSpeechSeq2SeqWithPadding）旨在将数据点批量打包，将每个批次中的attention_mask填充到最大长度，以保持批处理中张量形状的一致性，并用-100替换填充值，以便在损失函数中被忽略。这对于神经网络的高效训练至关重要。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cb4678c9-2d52-4b2f-b56d-07f1213d14fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import torch\n",
    "\n",
    "from dataclasses import dataclass\n",
    "from typing import Any, Dict, List, Union\n",
    "\n",
    "# 定义一个针对语音到文本任务的数据整理器类\n",
    "@dataclass\n",
    "class DataCollatorSpeechSeq2SeqWithPadding:\n",
    "    processor: Any  # 处理器结合了特征提取器和分词器\n",
    "\n",
    "    # 整理器函数，将特征列表处理成一个批次\n",
    "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
    "        # 从特征列表中提取输入特征，并填充以使它们具有相同的形状\n",
    "        input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
    "        batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
    "\n",
    "        # 从特征列表中提取标签特征（文本令牌），并进行填充\n",
    "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
    "        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
    "\n",
    "        # 使用-100替换标签中的填充区域，-100通常用于在损失计算中忽略填充令牌\n",
    "        labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
    "\n",
    "        # 如果批次中的所有序列都以句子开始令牌开头，则移除它\n",
    "        if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
    "            labels = labels[:, 1:]\n",
    "\n",
    "        # 将处理过的标签添加到批次中\n",
    "        batch[\"labels\"] = labels\n",
    "\n",
    "        return batch  # 返回最终的批次，准备好进行训练或评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8d94b010-3b27-41e6-bfab-711e5e8e60dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用给定的处理器实例化数据整理器\n",
    "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d3cf4b3-7bd7-4165-bf48-00f73eb72e59",
   "metadata": {},
   "source": [
    "## 模型准备\n",
    "加载预训练模型（int8 精度）\n",
    "使用 int8  精度加载预训练模型，进一步降低显存需求。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f72e96ae-4941-4e61-a0c8-75c9549aa8de",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForSpeechSeq2Seq\n",
    "model = AutoModelForSpeechSeq2Seq.from_pretrained(model_name_or_path, load_in_8bit=True, device_map=\"auto\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "65ed111b-5d88-4206-aa2d-10949379ad6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置模型配置中的forced_decoder_ids属性为None\n",
    "model.config.forced_decoder_ids = None  # 这通常用于指定在解码（生成文本）过程中必须使用的特定token的ID，设置为None表示没有这样的强制要求\n",
    "\n",
    "# 设置模型配置中的suppress_tokens列表为空\n",
    "model.config.suppress_tokens = []  # 这用于指定在生成过程中应被抑制（不生成）的token的列表，设置为空列表表示没有要抑制的token"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c473e4-522f-4271-8c1c-a6eb5e7c1d2e",
   "metadata": {},
   "source": [
    "### PEFT 微调前的模型处理\n",
    "在使用 peft 训练 int8 模型之前，需要进行一些预处理：\n",
    "\n",
    "- 将所有非 int8 精度模块转换为全精度（fp32）以保证稳定性\n",
    "- 为输入嵌入层添加一个 forward_hook，以启用输入隐藏状态的梯度计算\n",
    "- 启用梯度检查点以实现更高效的内存训练\n",
    "\n",
    "使用 peft 库预定义的工具函数 prepare_model_for_int8_training，便可自动完成以上模型处理工作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6557c0c2-c488-43b9-a00d-50d33090939f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/miniconda3/envs/transformers/lib/python3.11/site-packages/peft/utils/other.py:143: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from peft import prepare_model_for_int8_training\n",
    "\n",
    "model = prepare_model_for_int8_training(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effd9c8e-2d63-4565-8b14-6191d07b7fbe",
   "metadata": {},
   "source": [
    "## LoRA Adapter 配置"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c443550c-1a1e-40fd-8329-204298acfa68",
   "metadata": {},
   "source": [
    "在 peft 中使用LoRA非常简捷，借助 PeftModel抽象，我们可以快速使用低秩适配器（LoRA）到任意模型。\n",
    "\n",
    "通过使用 peft 中的 get_peft_model 工具函数来实现。\n",
    "\n",
    "#### 关于 LoRA 超参数的说明：\n",
    "\n",
    "MatMul(B,A) * Scaling\n",
    "\n",
    "Scaling = LoRA_Alpha / Rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d96d58d9-3b12-4caa-b046-f0f75a9f8f8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from peft import LoraConfig, PeftModel, LoraModel, LoraConfig, get_peft_model\n",
    "\n",
    "# 创建一个LoraConfig对象，用于设置LoRA（Low-Rank Adaptation）的配置参数\n",
    "config = LoraConfig(\n",
    "    r=4,  # LoRA的秩，影响LoRA矩阵的大小\n",
    "    lora_alpha=64,  # LoRA适应的比例因子\n",
    "    # 指定将LoRA应用到的模型模块，通常是attention和全连接层的投影。\n",
    "    target_modules=[\"q_proj\", \"v_proj\"],\n",
    "    lora_dropout=0.05,  # 在LoRA模块中使用的dropout率\n",
    "    bias=\"none\",  # 设置bias的使用方式，这里没有使用bias\n",
    ")\n",
    "# 将LoRA配置应用到模型配置中\n",
    "config.low_rank_adapters = config"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9835218b-0923-4e72-bae2-28b1ae712767",
   "metadata": {},
   "source": [
    "### 使用get_peft_model函数和给定的配置来获取一个PEFT模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "44b0bbb6-cced-48a8-aa5b-c98e35d450ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "peft_model = get_peft_model(model, config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d9ce9455-e5f4-417d-aed2-21b9d8727462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainable params: 1,966,080 || all params: 1,545,271,040 || trainable%: 0.12723204856023188\n"
     ]
    }
   ],
   "source": [
    "# 打印 LoRA 微调训练的模型参数\n",
    "peft_model.print_trainable_parameters()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba965858-d931-4bca-a46a-00d9469198b5",
   "metadata": {},
   "source": [
    "### 模型训练\n",
    "#### Seq2SeqTrainingArguments 训练参数\n",
    "关于设置训练步数和评估步数\n",
    "\n",
    "基于 epochs 设置：\n",
    "\n",
    "num_train_epochs=3,  # 训练的总轮数\n",
    "\n",
    "    evaluation_strategy=\"epoch\",  # 设置评估策略，这里是在每个epoch结束时进行评估\n",
    "    \n",
    "    warmup_steps=50,  # 在训练初期增加学习率的步数，有助于稳定训练\n",
    "\n",
    "基于 steps 设置：\n",
    "\n",
    "    max_steps=100, # 训练总步数\n",
    "    \n",
    "    evaluation_strategy=\"steps\", \n",
    "    \n",
    "    eval_steps=25, # 评估步数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "516d4956-9692-43af-b0c1-594bb33ae71a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import Seq2SeqTrainingArguments\n",
    "\n",
    "# 设置序列到序列模型训练的参数\n",
    "training_args = Seq2SeqTrainingArguments(\n",
    "    output_dir=model_dir,  # 指定模型输出和保存的目录\n",
    "    per_device_train_batch_size=batch_size,  # 每个设备上的训练批量大小\n",
    "    learning_rate=1e-3,  # 学习率\n",
    "    num_train_epochs=1,  # 训练的总轮数\n",
    "    evaluation_strategy=\"epoch\",  # 设置评估策略，这里是在每个epoch结束时进行评估\n",
    "    # warmup_steps=50,  # 在训练初期增加学习率的步数，有助于稳定训练\n",
    "    # fp16=True,  # 启用混合精度训练，可以提高训练速度，同时减少内存使用\n",
    "    per_device_eval_batch_size=batch_size,  # 每个设备上的评估批量大小\n",
    "    generation_max_length=128,  # 生成任务的最大长度\n",
    "    logging_steps=10,  # 指定日志记录的步骤，用于跟踪训练进度\n",
    "    remove_unused_columns=False,  # 是否删除不使用的列，以减少数据处理开销\n",
    "    label_names=[\"labels\"],  # 指定标签列的名称，用于训练过程中\n",
    "    # evaluation_strategy=\"steps\",\n",
    "    # eval_steps=25,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd12dab0-5284-440c-b036-47f3d2648562",
   "metadata": {},
   "source": [
    "### 实例化 Seq2SeqTrainer 训练器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9d5596c1-1542-4bab-94f9-cc0dd6f174d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import Seq2SeqTrainer\n",
    "\n",
    "trainer = Seq2SeqTrainer(\n",
    "    args=training_args,\n",
    "    model=peft_model,\n",
    "    train_dataset=tokenized_common_voice[\"train\"],\n",
    "    eval_dataset=tokenized_common_voice[\"validation\"],\n",
    "    data_collator=data_collator,\n",
    "    tokenizer=processor.feature_extractor,\n",
    ")\n",
    "peft_model.config.use_cache = False"
   ]
  },
  {
   "attachments": {
    "2a7a0216-ae50-4e41-a15d-3af158746e39.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "97034823-b11f-433c-9626-25485fb67ba3",
   "metadata": {},
   "source": [
    "![image.png](attachment:2a7a0216-ae50-4e41-a15d-3af158746e39.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "52b75456-da5e-4b06-b222-4c2bc7037051",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [100/100 25:48, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.314500</td>\n",
       "      <td>0.442596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=100, training_loss=0.5525292730331421, metrics={'train_runtime': 1560.9236, 'train_samples_per_second': 1.025, 'train_steps_per_second': 0.064, 'total_flos': 3.4016034816e+18, 'train_loss': 0.5525292730331421, 'epoch': 1.0})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2780d99e-c7ee-4a1a-96c1-0fd14b9e7600",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.save_model(model_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e8c17b23-9d33-49eb-89b1-ccf0cf9f3012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeftModel(\n",
       "  (base_model): LoraModel(\n",
       "    (model): WhisperForConditionalGeneration(\n",
       "      (model): WhisperModel(\n",
       "        (encoder): WhisperEncoder(\n",
       "          (conv1): Conv1d(80, 1280, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (conv2): Conv1d(1280, 1280, kernel_size=(3,), stride=(2,), padding=(1,))\n",
       "          (embed_positions): Embedding(1500, 1280)\n",
       "          (layers): ModuleList(\n",
       "            (0-31): 32 x WhisperEncoderLayer(\n",
       "              (self_attn): WhisperSdpaAttention(\n",
       "                (k_proj): Linear8bitLt(in_features=1280, out_features=1280, bias=False)\n",
       "                (v_proj): lora.Linear8bitLt(\n",
       "                  (base_layer): Linear8bitLt(in_features=1280, out_features=1280, bias=True)\n",
       "                  (lora_dropout): ModuleDict(\n",
       "                    (default): Dropout(p=0.05, inplace=False)\n",
       "                  )\n",
       "                  (lora_A): ModuleDict(\n",
       "                    (default): Linear(in_features=1280, out_features=4, bias=False)\n",
       "                  )\n",
       "                  (lora_B): ModuleDict(\n",
       "                    (default): Linear(in_features=4, out_features=1280, bias=False)\n",
       "                  )\n",
       "                  (lora_embedding_A): ParameterDict()\n",
       "                  (lora_embedding_B): ParameterDict()\n",
       "                )\n",
       "                (q_proj): lora.Linear8bitLt(\n",
       "                  (base_layer): Linear8bitLt(in_features=1280, out_features=1280, bias=True)\n",
       "                  (lora_dropout): ModuleDict(\n",
       "                    (default): Dropout(p=0.05, inplace=False)\n",
       "                  )\n",
       "                  (lora_A): ModuleDict(\n",
       "                    (default): Linear(in_features=1280, out_features=4, bias=False)\n",
       "                  )\n",
       "                  (lora_B): ModuleDict(\n",
       "                    (default): Linear(in_features=4, out_features=1280, bias=False)\n",
       "                  )\n",
       "                  (lora_embedding_A): ParameterDict()\n",
       "                  (lora_embedding_B): ParameterDict()\n",
       "                )\n",
       "                (out_proj): Linear8bitLt(in_features=1280, out_features=1280, bias=True)\n",
       "              )\n",
       "              (self_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "              (activation_fn): GELUActivation()\n",
       "              (fc1): Linear8bitLt(in_features=1280, out_features=5120, bias=True)\n",
       "              (fc2): Linear8bitLt(in_features=5120, out_features=1280, bias=True)\n",
       "              (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "          )\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (decoder): WhisperDecoder(\n",
       "          (embed_tokens): Embedding(51865, 1280, padding_idx=50257)\n",
       "          (embed_positions): WhisperPositionalEmbedding(448, 1280)\n",
       "          (layers): ModuleList(\n",
       "            (0-31): 32 x WhisperDecoderLayer(\n",
       "              (self_attn): WhisperSdpaAttention(\n",
       "                (k_proj): Linear8bitLt(in_features=1280, out_features=1280, bias=False)\n",
       "                (v_proj): lora.Linear8bitLt(\n",
       "                  (base_layer): Linear8bitLt(in_features=1280, out_features=1280, bias=True)\n",
       "                  (lora_dropout): ModuleDict(\n",
       "                    (default): Dropout(p=0.05, inplace=False)\n",
       "                  )\n",
       "                  (lora_A): ModuleDict(\n",
       "                    (default): Linear(in_features=1280, out_features=4, bias=False)\n",
       "                  )\n",
       "                  (lora_B): ModuleDict(\n",
       "                    (default): Linear(in_features=4, out_features=1280, bias=False)\n",
       "                  )\n",
       "                  (lora_embedding_A): ParameterDict()\n",
       "                  (lora_embedding_B): ParameterDict()\n",
       "                )\n",
       "                (q_proj): lora.Linear8bitLt(\n",
       "                  (base_layer): Linear8bitLt(in_features=1280, out_features=1280, bias=True)\n",
       "                  (lora_dropout): ModuleDict(\n",
       "                    (default): Dropout(p=0.05, inplace=False)\n",
       "                  )\n",
       "                  (lora_A): ModuleDict(\n",
       "                    (default): Linear(in_features=1280, out_features=4, bias=False)\n",
       "                  )\n",
       "                  (lora_B): ModuleDict(\n",
       "                    (default): Linear(in_features=4, out_features=1280, bias=False)\n",
       "                  )\n",
       "                  (lora_embedding_A): ParameterDict()\n",
       "                  (lora_embedding_B): ParameterDict()\n",
       "                )\n",
       "                (out_proj): Linear8bitLt(in_features=1280, out_features=1280, bias=True)\n",
       "              )\n",
       "              (activation_fn): GELUActivation()\n",
       "              (self_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "              (encoder_attn): WhisperSdpaAttention(\n",
       "                (k_proj): Linear8bitLt(in_features=1280, out_features=1280, bias=False)\n",
       "                (v_proj): lora.Linear8bitLt(\n",
       "                  (base_layer): Linear8bitLt(in_features=1280, out_features=1280, bias=True)\n",
       "                  (lora_dropout): ModuleDict(\n",
       "                    (default): Dropout(p=0.05, inplace=False)\n",
       "                  )\n",
       "                  (lora_A): ModuleDict(\n",
       "                    (default): Linear(in_features=1280, out_features=4, bias=False)\n",
       "                  )\n",
       "                  (lora_B): ModuleDict(\n",
       "                    (default): Linear(in_features=4, out_features=1280, bias=False)\n",
       "                  )\n",
       "                  (lora_embedding_A): ParameterDict()\n",
       "                  (lora_embedding_B): ParameterDict()\n",
       "                )\n",
       "                (q_proj): lora.Linear8bitLt(\n",
       "                  (base_layer): Linear8bitLt(in_features=1280, out_features=1280, bias=True)\n",
       "                  (lora_dropout): ModuleDict(\n",
       "                    (default): Dropout(p=0.05, inplace=False)\n",
       "                  )\n",
       "                  (lora_A): ModuleDict(\n",
       "                    (default): Linear(in_features=1280, out_features=4, bias=False)\n",
       "                  )\n",
       "                  (lora_B): ModuleDict(\n",
       "                    (default): Linear(in_features=4, out_features=1280, bias=False)\n",
       "                  )\n",
       "                  (lora_embedding_A): ParameterDict()\n",
       "                  (lora_embedding_B): ParameterDict()\n",
       "                )\n",
       "                (out_proj): Linear8bitLt(in_features=1280, out_features=1280, bias=True)\n",
       "              )\n",
       "              (encoder_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "              (fc1): Linear8bitLt(in_features=1280, out_features=5120, bias=True)\n",
       "              (fc2): Linear8bitLt(in_features=5120, out_features=1280, bias=True)\n",
       "              (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "            )\n",
       "          )\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "      (proj_out): Linear(in_features=1280, out_features=51865, bias=False)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peft_model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d37904-0473-49a0-b4e7-c0694e0c4d0a",
   "metadata": {},
   "source": [
    "### 模型推理（可能需要重启 Notebook）\r\n",
    "再次加载模型会额外占用显存，如果显存已经达到上限，建议重启 Notebook 后再进行以下操作\r\n",
    "\r\n",
    "使用 PeftModel 加载 LoRA 微调后 Whisper 模型\r\n",
    "使用 PeftConfig 加载 LoRA Adapter 配置参数，使用 PeftModel 加载微调后 Whisper 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e8faf51e-f136-4117-81a9-1f8daa6b70ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dir = \"models/whisper-large-v2-asr-int8\"\n",
    "\n",
    "language = \"Chinese (China)\"\n",
    "language_abbr = \"zh-CN\"\n",
    "language_decode = \"chinese\"\n",
    "task = \"transcribe\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4627dc74-bbc6-4708-b9e5-500a48ed3576",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/miniconda3/envs/transformers/lib/python3.11/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n",
      "2024-02-28 23:43:03.612565: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-02-28 23:43:04.073985: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-02-28 23:43:04.074064: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-02-28 23:43:04.139758: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-02-28 23:43:04.331262: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-02-28 23:43:06.098301: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "/root/miniconda3/envs/transformers/lib/python3.11/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
      "  _torch_pytree._register_pytree_node(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3af43f5788864cafaab731e47faea4f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f9dcf10de4b468588b2724ca8402296",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/6.17G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fade9d9094b849b3909cea3923f4af29",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/4.29k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoModelForSpeechSeq2Seq, AutoTokenizer, AutoProcessor\n",
    "from peft import PeftConfig, PeftModel\n",
    "\n",
    "peft_config = PeftConfig.from_pretrained(model_dir)\n",
    "\n",
    "base_model = AutoModelForSpeechSeq2Seq.from_pretrained(\n",
    "    peft_config.base_model_name_or_path, load_in_8bit=True, device_map=\"auto\"\n",
    ")\n",
    "\n",
    "peft_model = PeftModel.from_pretrained(base_model, model_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5fa7e9cc-994a-48c9-bce2-c77176d13599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2b6139636af48b99105fe8686f920ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/805 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b41d8fb641a74662829f5391da6eb77b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76cd2956616f42228ef2aad990098eb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c81c7b0775849c7a004ce9449fb247c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67e66698996543b588b152bd1e431d02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c246cfcc2a81494f9c27ededa3095ec1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba12162e4f664760baf375ed89302c5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/2.08k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e5ade8b37154a1393e44299a334e85f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "preprocessor_config.json:   0%|          | 0.00/185k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)\n",
    "processor = AutoProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)\n",
    "feature_extractor = processor.feature_extractor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "819aa24e-e2cd-4317-a9d6-95de145f8ebb",
   "metadata": {},
   "source": [
    "### 使用 Pipeline API 部署微调后 Whisper 实现中文语音识别任务"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db480057-46be-40da-a20f-e4822b638ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_audio = \"data/audio/mlk.flac\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c1d85b4-5fc4-49aa-8de1-835f8ba26977",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The model 'PeftModel' is not supported for . Supported models are ['Pop2PianoForConditionalGeneration', 'SeamlessM4TForSpeechToText', 'SeamlessM4Tv2ForSpeechToText', 'SpeechEncoderDecoderModel', 'Speech2TextForConditionalGeneration', 'SpeechT5ForSpeechToText', 'WhisperForConditionalGeneration', 'Data2VecAudioForCTC', 'HubertForCTC', 'MCTCTForCTC', 'SEWForCTC', 'SEWDForCTC', 'UniSpeechForCTC', 'UniSpeechSatForCTC', 'Wav2Vec2ForCTC', 'Wav2Vec2ConformerForCTC', 'WavLMForCTC'].\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutomaticSpeechRecognitionPipeline\n",
    "\n",
    "pipeline = AutomaticSpeechRecognitionPipeline(model=peft_model, tokenizer=tokenizer, feature_extractor=feature_extractor)\n",
    "\n",
    "forced_decoder_ids = processor.get_decoder_prompt_ids(language=language_decode, task=task)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3898fa1-e141-4a4a-bfc6-a4aeafae0179",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/miniconda3/envs/transformers/lib/python3.11/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n",
      "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "with torch.cuda.amp.autocast():\n",
    "    text = pipeline(test_audio, max_new_tokens=255)[\"text\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a655894d-9a23-4651-a588-6ee839d3b3ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8da5ea64-3e00-458a-9a1e-4c6e27e251d0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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