398 lines
14 KiB
Markdown
398 lines
14 KiB
Markdown
<!--
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Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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# 自动语音识别
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[[open-in-colab]]
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<Youtube id="TksaY_FDgnk"/>
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自动语音识别（ASR）将语音信号转换为文本，将一系列音频输入映射到文本输出。
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Siri 和 Alexa 这类虚拟助手使用 ASR 模型来帮助用户日常生活，还有许多其他面向用户的有用应用，如会议实时字幕和会议纪要。
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本指南将向您展示如何：
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1. 在 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 数据集上对
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[Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) 进行微调，以将音频转录为文本。
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2. 使用微调后的模型进行推断。
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<Tip>
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本教程中展示的任务受以下模型架构的支持：
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<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
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[Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [M-CTC-T](../model_doc/mctct), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-BERT](../model_doc/wav2vec2-bert), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)
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<!--End of the generated tip-->
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</Tip>
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在开始之前，请确保您已安装所有必要的库：
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```bash
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pip install transformers datasets evaluate jiwer
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```
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我们鼓励您登录自己的 Hugging Face 账户，这样您就可以上传并与社区分享您的模型。
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出现提示时，输入您的令牌登录：
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```py
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>>> from huggingface_hub import notebook_login
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>>> notebook_login()
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```
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## 加载 MInDS-14 数据集
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首先从🤗 Datasets 库中加载 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14)
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数据集的一个较小子集。这将让您有机会先进行实验，确保一切正常，然后再花更多时间在完整数据集上进行训练。
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```py
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>>> from datasets import load_dataset, Audio
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>>> minds = load_dataset("PolyAI/minds14", name="en-US", split="train[:100]")
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```
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使用 [`~Dataset.train_test_split`] 方法将数据集的 `train` 拆分为训练集和测试集：
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```py
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>>> minds = minds.train_test_split(test_size=0.2)
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```
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然后看看数据集：
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```py
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>>> minds
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DatasetDict({
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train: Dataset({
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features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
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num_rows: 16
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})
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test: Dataset({
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features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
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num_rows: 4
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})
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})
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```
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虽然数据集包含 `lang_id `和 `english_transcription` 等许多有用的信息，但在本指南中，
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您将专注于 `audio` 和 `transcription`。使用 [`~datasets.Dataset.remove_columns`] 方法删除其他列：
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```py
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>>> minds = minds.remove_columns(["english_transcription", "intent_class", "lang_id"])
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```
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再看看示例：
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```py
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>>> minds["train"][0]
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{'audio': {'array': array([-0.00024414, 0. , 0. , ..., 0.00024414,
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0.00024414, 0.00024414], dtype=float32),
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'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
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'sampling_rate': 8000},
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'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
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'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
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```
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有 2 个字段：
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- `audio`：由语音信号形成的一维 `array`，用于加载和重新采样音频文件。
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- `transcription`：目标文本。
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## 预处理
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下一步是加载一个 Wav2Vec2 处理器来处理音频信号：
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```py
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>>> from transformers import AutoProcessor
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>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base")
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```
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MInDS-14 数据集的采样率为 8000kHz（您可以在其[数据集卡片](https://huggingface.co/datasets/PolyAI/minds14)中找到此信息），
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这意味着您需要将数据集重新采样为 16000kHz 以使用预训练的 Wav2Vec2 模型：
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```py
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>>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000))
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>>> minds["train"][0]
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{'audio': {'array': array([-2.38064706e-04, -1.58618059e-04, -5.43987835e-06, ...,
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2.78103951e-04, 2.38446111e-04, 1.18740834e-04], dtype=float32),
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'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
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'sampling_rate': 16000},
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'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
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'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
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```
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如您在上面的 `transcription` 中所看到的，文本包含大小写字符的混合。
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Wav2Vec2 分词器仅训练了大写字符，因此您需要确保文本与分词器的词汇表匹配：
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```py
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>>> def uppercase(example):
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... return {"transcription": example["transcription"].upper()}
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>>> minds = minds.map(uppercase)
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```
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现在创建一个预处理函数，该函数应该：
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1. 调用 `audio` 列以加载和重新采样音频文件。
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2. 从音频文件中提取 `input_values` 并使用处理器对 `transcription` 列执行 tokenizer 操作。
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```py
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>>> def prepare_dataset(batch):
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... audio = batch["audio"]
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... batch = processor(audio["array"], sampling_rate=audio["sampling_rate"], text=batch["transcription"])
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... batch["input_length"] = len(batch["input_values"][0])
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... return batch
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```
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要在整个数据集上应用预处理函数，可以使用🤗 Datasets 的 [`~datasets.Dataset.map`] 函数。
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您可以通过增加 `num_proc` 参数来加速 `map` 的处理进程数量。
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使用 [`~datasets.Dataset.remove_columns`] 方法删除不需要的列：
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```py
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>>> encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names["train"], num_proc=4)
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```
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🤗 Transformers 没有用于 ASR 的数据整理器，因此您需要调整 [`DataCollatorWithPadding`] 来创建一个示例批次。
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它还会动态地将您的文本和标签填充到其批次中最长元素的长度（而不是整个数据集），以使它们具有统一的长度。
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虽然可以通过在 `tokenizer` 函数中设置 `padding=True` 来填充文本，但动态填充更有效。
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与其他数据整理器不同，这个特定的数据整理器需要对 `input_values` 和 `labels `应用不同的填充方法：
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```py
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>>> import torch
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>>> from dataclasses import dataclass, field
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>>> from typing import Any, Dict, List, Optional, Union
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>>> @dataclass
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... class DataCollatorCTCWithPadding:
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... processor: AutoProcessor
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... padding: Union[bool, str] = "longest"
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... def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
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... # split inputs and labels since they have to be of different lengths and need
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... # different padding methods
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... input_features = [{"input_values": feature["input_values"][0]} for feature in features]
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... label_features = [{"input_ids": feature["labels"]} for feature in features]
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... batch = self.processor.pad(input_features, padding=self.padding, return_tensors="pt")
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... labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors="pt")
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... # replace padding with -100 to ignore loss correctly
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... labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
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... batch["labels"] = labels
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... return batch
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```
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现在实例化您的 `DataCollatorForCTCWithPadding`：
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```py
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>>> data_collator = DataCollatorCTCWithPadding(processor=processor, padding="longest")
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```
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## 评估
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在训练过程中包含一个指标通常有助于评估模型的性能。
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您可以通过🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) 库快速加载一个评估方法。
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对于这个任务，加载 [word error rate](https://huggingface.co/spaces/evaluate-metric/wer)（WER）指标
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（请参阅🤗 Evaluate [快速上手](https://huggingface.co/docs/evaluate/a_quick_tour)以了解如何加载和计算指标）：
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```py
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>>> import evaluate
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>>> wer = evaluate.load("wer")
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```
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然后创建一个函数，将您的预测和标签传递给 [`~evaluate.EvaluationModule.compute`] 来计算 WER：
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```py
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>>> import numpy as np
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>>> def compute_metrics(pred):
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... pred_logits = pred.predictions
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... pred_ids = np.argmax(pred_logits, axis=-1)
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... pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
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... pred_str = processor.batch_decode(pred_ids)
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... label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
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... wer = wer.compute(predictions=pred_str, references=label_str)
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... return {"wer": wer}
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```
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您的 `compute_metrics` 函数现在已经准备就绪，当您设置好训练时将返回给此函数。
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## 训练
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<frameworkcontent>
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<pt>
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<Tip>
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如果您不熟悉使用[`Trainer`]微调模型，请查看这里的基本教程[here](../training#train-with-pytorch-trainer)！
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</Tip>
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现在您已经准备好开始训练您的模型了！使用 [`AutoModelForCTC`] 加载 Wav2Vec2。
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使用 `ctc_loss_reduction` 参数指定要应用的减少方式。通常最好使用平均值而不是默认的求和：
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```py
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>>> from transformers import AutoModelForCTC, TrainingArguments, Trainer
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>>> model = AutoModelForCTC.from_pretrained(
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... "facebook/wav2vec2-base",
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... ctc_loss_reduction="mean",
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... pad_token_id=processor.tokenizer.pad_token_id,
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)
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```
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此时，只剩下 3 个步骤：
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1. 在 [`TrainingArguments`] 中定义您的训练参数。唯一必需的参数是 `output_dir`，用于指定保存模型的位置。
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您可以通过设置 `push_to_hub=True` 将此模型推送到 Hub（您需要登录到 Hugging Face 才能上传您的模型）。
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在每个 epoch 结束时，[`Trainer`] 将评估 WER 并保存训练检查点。
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2. 将训练参数与模型、数据集、分词器、数据整理器和 `compute_metrics` 函数一起传递给 [`Trainer`]。
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3. 调用 [`~Trainer.train`] 来微调您的模型。
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```py
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>>> training_args = TrainingArguments(
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... output_dir="my_awesome_asr_mind_model",
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... per_device_train_batch_size=8,
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... gradient_accumulation_steps=2,
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... learning_rate=1e-5,
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... warmup_steps=500,
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... max_steps=2000,
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... gradient_checkpointing=True,
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... fp16=True,
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... group_by_length=True,
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... evaluation_strategy="steps",
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... per_device_eval_batch_size=8,
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... save_steps=1000,
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... eval_steps=1000,
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... logging_steps=25,
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... load_best_model_at_end=True,
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... metric_for_best_model="wer",
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... greater_is_better=False,
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... push_to_hub=True,
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... )
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>>> trainer = Trainer(
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... model=model,
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... args=training_args,
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... train_dataset=encoded_minds["train"],
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... eval_dataset=encoded_minds["test"],
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... tokenizer=processor,
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... data_collator=data_collator,
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... compute_metrics=compute_metrics,
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... )
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>>> trainer.train()
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```
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训练完成后，使用 [`~transformers.Trainer.push_to_hub`] 方法将您的模型分享到 Hub，方便大家使用您的模型：
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```py
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>>> trainer.push_to_hub()
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```
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</pt>
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</frameworkcontent>
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<Tip>
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要深入了解如何微调模型进行自动语音识别，
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请查看这篇博客[文章](https://huggingface.co/blog/fine-tune-wav2vec2-english)以了解英语 ASR，
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还可以参阅[这篇文章](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)以了解多语言 ASR。
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</Tip>
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## 推断
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很好，现在您已经微调了一个模型，您可以用它进行推断了！
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加载您想要运行推断的音频文件。请记住，如果需要，将音频文件的采样率重新采样为与模型匹配的采样率！
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```py
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>>> from datasets import load_dataset, Audio
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>>> dataset = load_dataset("PolyAI/minds14", "en-US", split="train")
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>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
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>>> sampling_rate = dataset.features["audio"].sampling_rate
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>>> audio_file = dataset[0]["audio"]["path"]
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```
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尝试使用微调后的模型进行推断的最简单方法是使用 [`pipeline`]。
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使用您的模型实例化一个用于自动语音识别的 `pipeline`，并将您的音频文件传递给它：
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```py
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>>> from transformers import pipeline
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>>> transcriber = pipeline("automatic-speech-recognition", model="stevhliu/my_awesome_asr_minds_model")
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>>> transcriber(audio_file)
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{'text': 'I WOUD LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'}
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```
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<Tip>
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转录结果还不错，但可以更好！尝试用更多示例微调您的模型，以获得更好的结果！
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</Tip>
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如果您愿意，您也可以手动复制 `pipeline` 的结果：
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<frameworkcontent>
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<pt>
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加载一个处理器来预处理音频文件和转录，并将 `input` 返回为 PyTorch 张量：
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```py
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>>> from transformers import AutoProcessor
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>>> processor = AutoProcessor.from_pretrained("stevhliu/my_awesome_asr_mind_model")
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>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
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```
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将您的输入传递给模型并返回 logits：
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```py
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>>> from transformers import AutoModelForCTC
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>>> model = AutoModelForCTC.from_pretrained("stevhliu/my_awesome_asr_mind_model")
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>>> with torch.no_grad():
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... logits = model(**inputs).logits
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```
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获取具有最高概率的预测 `input_ids`，并使用处理器将预测的 `input_ids` 解码回文本：
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```py
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>>> import torch
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>>> predicted_ids = torch.argmax(logits, dim=-1)
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>>> transcription = processor.batch_decode(predicted_ids)
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>>> transcription
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['I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER']
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```
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</pt>
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</frameworkcontent> |