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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- librispeech_asr |
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metrics: |
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- f1 |
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base_model: facebook/wav2vec2-xls-r-300m |
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model-index: |
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- name: weights |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-large-xlsr-53-gender-recognition-librispeech |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on Librispeech-clean-100 for gender recognition. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0061 |
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- F1: 0.9993 |
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### Compute your inferences |
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```python |
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import os |
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from typing import List, Optional, Union, Dict |
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import tqdm |
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import torch |
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import torchaudio |
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import numpy as np |
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import pandas as pd |
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from torch import nn |
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from torch.utils.data import DataLoader |
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from torch.nn import functional as F |
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from transformers import ( |
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AutoFeatureExtractor, |
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AutoModelForAudioClassification, |
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Wav2Vec2Processor |
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) |
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class CustomDataset(torch.utils.data.Dataset): |
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def __init__( |
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self, |
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dataset: List, |
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basedir: Optional[str] = None, |
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sampling_rate: int = 16000, |
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max_audio_len: int = 5, |
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): |
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self.dataset = dataset |
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self.basedir = basedir |
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self.sampling_rate = sampling_rate |
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self.max_audio_len = max_audio_len |
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def __len__(self): |
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""" |
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Return the length of the dataset |
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""" |
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return len(self.dataset) |
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def _cutorpad(self, audio: np.ndarray) -> np.ndarray: |
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""" |
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Cut or pad audio to the wished length |
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""" |
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effective_length = self.sampling_rate * self.max_audio_len |
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len_audio = len(audio) |
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# If audio length is bigger than wished audio length |
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if len_audio > effective_length: |
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audio = audio[:effective_length] |
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# Expand one dimension related to the channel dimension |
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return audio |
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def __getitem__(self, index) -> torch.Tensor: |
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""" |
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Return the audio and the sampling rate |
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""" |
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if self.basedir is None: |
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filepath = self.dataset[index] |
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else: |
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filepath = os.path.join(self.basedir, self.dataset[index]) |
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speech_array, sr = torchaudio.load(filepath) |
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# Transform to mono |
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if speech_array.shape[0] > 1: |
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speech_array = torch.mean(speech_array, dim=0, keepdim=True) |
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if sr != self.sampling_rate: |
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transform = torchaudio.transforms.Resample(sr, self.sampling_rate) |
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speech_array = transform(speech_array) |
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sr = self.sampling_rate |
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speech_array = speech_array.squeeze().numpy() |
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# Cut or pad audio |
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speech_array = self._cutorpad(speech_array) |
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return speech_array |
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class CollateFunc: |
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def __init__( |
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self, |
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processor: Wav2Vec2Processor, |
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max_length: Optional[int] = None, |
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padding: Union[bool, str] = True, |
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pad_to_multiple_of: Optional[int] = None, |
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sampling_rate: int = 16000, |
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): |
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self.padding = padding |
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self.processor = processor |
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self.max_length = max_length |
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self.sampling_rate = sampling_rate |
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self.pad_to_multiple_of = pad_to_multiple_of |
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def __call__(self, batch: List): |
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input_features = [] |
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for audio in batch: |
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input_tensor = self.processor(audio, sampling_rate=self.sampling_rate).input_values |
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input_tensor = np.squeeze(input_tensor) |
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input_features.append({"input_values": input_tensor}) |
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batch = self.processor.pad( |
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input_features, |
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padding=self.padding, |
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max_length=self.max_length, |
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pad_to_multiple_of=self.pad_to_multiple_of, |
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return_tensors="pt", |
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) |
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return batch |
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def predict(test_dataloader, model, device: torch.device): |
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""" |
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Predict the class of the audio |
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""" |
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model.to(device) |
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model.eval() |
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preds = [] |
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with torch.no_grad(): |
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for batch in tqdm.tqdm(test_dataloader): |
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input_values, attention_mask = batch['input_values'].to(device), batch['attention_mask'].to(device) |
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logits = model(input_values, attention_mask=attention_mask).logits |
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scores = F.softmax(logits, dim=-1) |
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pred = torch.argmax(scores, dim=1).cpu().detach().numpy() |
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preds.extend(pred) |
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return preds |
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def get_gender(model_name_or_path: str, audio_paths: List[str], label2id: Dict, id2label: Dict, device: torch.device): |
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num_labels = 2 |
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_name_or_path) |
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model = AutoModelForAudioClassification.from_pretrained( |
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pretrained_model_name_or_path=model_name_or_path, |
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num_labels=num_labels, |
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label2id=label2id, |
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id2label=id2label, |
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) |
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test_dataset = CustomDataset(audio_paths) |
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data_collator = CollateFunc( |
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processor=feature_extractor, |
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padding=True, |
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sampling_rate=16000, |
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) |
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test_dataloader = DataLoader( |
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dataset=test_dataset, |
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batch_size=16, |
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collate_fn=data_collator, |
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shuffle=False, |
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num_workers=10 |
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) |
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preds = predict(test_dataloader=test_dataloader, model=model, device=device) |
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return preds |
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model_name_or_path = "alefiury/wav2vec2-large-xlsr-53-gender-recognition-librispeech" |
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audio_paths = [] # Must be a list with absolute paths of the audios that will be used in inference |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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label2id = { |
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"female": 0, |
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"male": 1 |
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} |
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id2label = { |
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0: "female", |
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1: "male" |
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} |
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num_labels = 2 |
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preds = get_gender(model_name_or_path, audio_paths, label2id, id2label, device) |
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``` |
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## Training and evaluation data |
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The Librispeech-clean-100 dataset was used to train the model, with 70% of the data used for training, 10% for validation, and 20% for testing. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.002 | 1.0 | 1248 | 0.0061 | 0.9993 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.0+cu118 |
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- Tokenizers 0.13.3 |
