Wav2vec2 Large Xlsr 53 Gender Recognition Librispeech

🚀 wav2vec2-large-xlsr-53-gender-recognition-librispeech

該模型是 facebook/wav2vec2-xls-r-300m 在 Librispeech-clean-100 數據集上針對性別識別任務進行微調後的版本。它在評估集上取得了以下成績：

• 損失值：0.0061
• F1 分數：0.9993

🚀 快速開始

計算推理結果

import os
import random
from glob import glob
from typing import List, Optional, Union, Dict

import tqdm
import torch
import torchaudio
import numpy as np
import pandas as pd
from torch import nn
from torch.utils.data import DataLoader
from torch.nn import functional as F
from transformers import (
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    Wav2Vec2Processor
)

class CustomDataset(torch.utils.data.Dataset):
    def __init__(
        self,
        dataset: List,
        basedir: Optional[str] = None,
        sampling_rate: int = 16000,
        max_audio_len: int = 5,
    ):
        self.dataset = dataset
        self.basedir = basedir

        self.sampling_rate = sampling_rate
        self.max_audio_len = max_audio_len

    def __len__(self):
        """
        Return the length of the dataset
        """
        return len(self.dataset)

    def __getitem__(self, index):
        if self.basedir is None:
            filepath = self.dataset[index]
        else:
            filepath = os.path.join(self.basedir, self.dataset[index])

        speech_array, sr = torchaudio.load(filepath)

        if speech_array.shape[0] > 1:
            speech_array = torch.mean(speech_array, dim=0, keepdim=True)

        if sr != self.sampling_rate:
            transform = torchaudio.transforms.Resample(sr, self.sampling_rate)
            speech_array = transform(speech_array)
            sr = self.sampling_rate

        len_audio = speech_array.shape[1]

        # Pad or truncate the audio to match the desired length
        if len_audio < self.max_audio_len * self.sampling_rate:
            # Pad the audio if it's shorter than the desired length
            padding = torch.zeros(1, self.max_audio_len * self.sampling_rate - len_audio)
            speech_array = torch.cat([speech_array, padding], dim=1)
        else:
            # Truncate the audio if it's longer than the desired length
            speech_array = speech_array[:, :self.max_audio_len * self.sampling_rate]

        speech_array = speech_array.squeeze().numpy()

        return {"input_values": speech_array, "attention_mask": None}


class CollateFunc:
    def __init__(
        self,
        processor: Wav2Vec2Processor,
        padding: Union[bool, str] = True,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: bool = True,
        sampling_rate: int = 16000,
        max_length: Optional[int] = None,
    ):
        self.sampling_rate = sampling_rate
        self.processor = processor
        self.padding = padding
        self.pad_to_multiple_of = pad_to_multiple_of
        self.return_attention_mask = return_attention_mask
        self.max_length = max_length

    def __call__(self, batch: List[Dict[str, np.ndarray]]):
        # Extract input_values from the batch
        input_values = [item["input_values"] for item in batch]

        batch = self.processor(
            input_values,
            sampling_rate=self.sampling_rate,
            return_tensors="pt",
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_attention_mask=self.return_attention_mask
        )

        return {
            "input_values": batch.input_values,
            "attention_mask": batch.attention_mask if self.return_attention_mask else None
        }


def predict(test_dataloader, model, device: torch.device):
    """
    Predict the class of the audio
    """
    model.to(device)
    model.eval()
    preds = []

    with torch.no_grad():
        for batch in tqdm.tqdm(test_dataloader):
            input_values, attention_mask = batch['input_values'].to(device), batch['attention_mask'].to(device)

            logits = model(input_values, attention_mask=attention_mask).logits
            scores = F.softmax(logits, dim=-1)

            pred = torch.argmax(scores, dim=1).cpu().detach().numpy()

            preds.extend(pred)

    return preds


def get_gender(model_name_or_path: str, audio_paths: List[str], label2id: Dict, id2label: Dict, device: torch.device):
    num_labels = 2

    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name_or_path)
    model = AutoModelForAudioClassification.from_pretrained(
        pretrained_model_name_or_path=model_name_or_path,
        num_labels=num_labels,
        label2id=label2id,
        id2label=id2label,
    )

    test_dataset = CustomDataset(audio_paths, max_audio_len=5)  # for 5-second audio

    data_collator = CollateFunc(
        processor=feature_extractor,
        padding=True,
        sampling_rate=16000,
    )

    test_dataloader = DataLoader(
        dataset=test_dataset,
        batch_size=16,
        collate_fn=data_collator,
        shuffle=False,
        num_workers=2
    )

    preds = predict(test_dataloader=test_dataloader, model=model, device=device)

    return preds

model_name_or_path = "alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech"

audio_paths = [] # Must be a list with absolute paths of the audios that will be used in inference
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

label2id = {
    "female": 0,
    "male": 1
}

id2label = {
    0: "female",
    1: "male"
}

num_labels = 2

preds = get_gender(model_name_or_path, audio_paths, label2id, id2label, device)

📚 詳細文檔

訓練和評估數據

該模型使用了 Librispeech-clean-100 數據集進行訓練，其中 70% 的數據用於訓練，10% 用於驗證，20% 用於測試。

訓練超參數

訓練過程中使用了以下超參數：

• 學習率：3e-05
• 訓練批次大小：4
• 評估批次大小：4
• 隨機種子：42
• 梯度累積步數：4
• 總訓練批次大小：16
• 優化器：Adam（β1=0.9，β2=0.999，ε=1e-08）
• 學習率調度器類型：線性
• 學習率調度器熱身比例：0.1
• 訓練輪數：1
• 混合精度訓練：原生自動混合精度（Native AMP）

訓練結果

框架版本

• Transformers 4.28.0
• Pytorch 2.0.0+cu118
• Tokenizers 0.13.3

📄 許可證

本項目採用 Apache-2.0 許可證。