--- pipeline_tag: audio-classification --- # Model Introduction ## Highlight - The model is based on wav2vec2-base and fine-tuned with iemocap and Emotional Speech Dataset (ESD) data, so it supports Chinese and English audio. - The accuracy is as high as 92.9% - The model card shows only part of the source code.See Files and Versions for details - The model can predict the emotion of anger ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66690b1773d203101159bb96/QULPUWRcNuKEyGbN0by0g.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/66690b1773d203101159bb96/EBVUV9R9TT3E22MFk8bA0.png) #### Some details are as follows ```python import logging import pathlib import re import sys import time import csv from dataclasses import dataclass, field from typing import Any, Callable, Dict, List, Optional, Set, Union import datasets import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from packaging import version from torch.cuda.amp import GradScaler, autocast import librosa from lang_trans import arabic from datasets import Dataset import soundfile as sf from model import Wav2Vec2ForCTCnCLS from transformers.trainer_utils import get_last_checkpoint from transformers import ( HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, is_apex_available, trainer_utils, ) local_model_path = "local_model" if is_apex_available(): from apex import amp if version.parse(torch.__version__) >= version.parse("1.6"): _is_native_amp_available = True from torch.cuda.amp import autocast logger = logging.getLogger(__name__) @dataclass class TrainingArguments(TrainingArguments): output_dir: str = field( default="output/angry_tmp", metadata={"help": "The store of your output."}) do_predict: bool = field( default=True, metadata={"help": "The store of your output."}) do_eval: bool = field( default=False, metadata={"help": "The store of your output."}) overwrite_output_dir: str = field( default='overwrite_output_dir', metadata={"help": "The store of your output."} ) per_device_eval_batch_size: int = field( default=2, metadata={"help": "The store of your output."}) warmup_ratio: float = field( default=0.1, metadata={"help": "Linear warmup over warmup_ratio fraction of total steps."} ) @dataclass class DataCollatorCTCWithPadding: """ Data collator that will dynamically pad the inputs received. Args: processor (:class:`~transformers.Wav2Vec2Processor`) The processor used for proccessing the data. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (:obj:`int`, `optional`): Maximum length of the ``input_values`` of the returned list and optionally padding length (see above). max_length_labels (:obj:`int`, `optional`): Maximum length of the ``labels`` returned list and optionally padding length (see above). pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ processor: Wav2Vec2Processor padding: Union[bool, str] = True max_length: Optional[int] = None max_length_labels: Optional[int] = None pad_to_multiple_of: Optional[int] = None pad_to_multiple_of_labels: Optional[int] = None audio_only = False duration = 6 sample_rate = 16000 def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # split inputs and labels since they have to be of different lenghts and need # different padding methods input_features = [{"input_values": feature["input_values"]} for feature in features] batch = self.processor.pad( input_features, padding=self.padding, # max_length=self.max_length, max_length=self.duration*self.sample_rate, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) return batch class CTCTrainer(Trainer): def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]: self.use_amp = False self.use_apex = False self.deepspeed = False self.scaler = GradScaler() for k, v in inputs.items(): if isinstance(v, torch.Tensor): kwargs = dict(device=self.args.device) if self.deepspeed and inputs[k].dtype != torch.int64: kwargs.update(dict(dtype=self.args.hf_deepspeed_config.dtype())) inputs[k] = v.to(**kwargs) if self.args.past_index >= 0 and self._past is not None: inputs["mems"] = self._past return inputs def create_dataset(audio_path): data = { 'file': [audio_path] } dataset = Dataset.from_dict(data) return dataset def exeute_angry_predict(audio_path): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. target_sr = 16000 parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() configure_logger(model_args, training_args) orthography = Orthography.from_name(data_args.orthography.lower()) orthography.tokenizer = model_args.tokenizer processor = orthography.create_processor(model_args) if data_args.dataset_name == 'emotion': val_dataset = create_dataset(audio_path) cls_label_map = {"neutral":0, "angry":1} model = Wav2Vec2ForCTCnCLS.from_pretrained( local_model_path, gradient_checkpointing=True, # training_args.gradient_checkpointing, cls_len=len(cls_label_map), ) def prepare_example(example, audio_only=False): # TODO(elgeish) make use of multiprocessing? example["speech"], example["sampling_rate"] = librosa.load(example[data_args.speech_file_column], sr=target_sr) orig_sample_rate = example["sampling_rate"] target_sample_rate = target_sr if orig_sample_rate != target_sample_rate: example["speech"] = librosa.resample(example["speech"], orig_sr=orig_sample_rate, target_sr=target_sample_rate) if data_args.max_duration_in_seconds is not None: example["duration_in_seconds"] = len(example["speech"]) / example["sampling_rate"] return example if training_args.do_predict: val_dataset = val_dataset.map(prepare_example, fn_kwargs={'audio_only':True}) def prepare_dataset(batch, audio_only=False): # check that all files have the correct sampling rate assert ( len(set(batch["sampling_rate"])) == 1 ), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}." batch["input_values"] = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0]).input_values return batch if training_args.do_predict: val_dataset = val_dataset.map( prepare_dataset, fn_kwargs={'audio_only':True}, batch_size=training_args.per_device_eval_batch_size, batched=True, num_proc=data_args.preprocessing_num_workers, ) data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True) if model_args.freeze_feature_extractor: model.freeze_feature_extractor() trainer = CTCTrainer( model=model, args=training_args, eval_dataset=val_dataset, tokenizer=processor.feature_extractor, ) if training_args.do_predict: logger.info('******* Predict ********') data_collator.audio_only=True results= {} result= '' predictions, labels, metrics = trainer.predict(val_dataset, metric_key_prefix="predict") logits_ctc, logits_cls = predictions pred_ids = np.argmax(logits_cls, axis=-1) if pred_ids==0: result = "neutral" if pred_ids==1: result = "angry" results[audio_path] = result print("results", results) if __name__ == "__main__": audio_path = 'audio.mp3' exeute_angry_predict(audio_path) ```