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README.md
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README.md
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---
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language: en
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datasets:
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- aesdd
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tags:
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- audio
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- audio-classification
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- speech
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license: apache-2.0
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---
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~~~
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# requirement packages
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!pip install git+https://github.com/huggingface/datasets.git
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!pip install git+https://github.com/huggingface/transformers.git
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!pip install torchaudio
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!pip install librosa
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~~~
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# prediction
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~~~
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchaudio
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from transformers import AutoConfig, Wav2Vec2FeatureExtractor
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import librosa
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import IPython.display as ipd
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import numpy as np
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import pandas as pd
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~~~
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~~~
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model_name_or_path = "harshit345/xlsr-wav2vec-speech-emotion-recognition"
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config = AutoConfig.from_pretrained(model_name_or_path)
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
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sampling_rate = feature_extractor.sampling_rate
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model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path).to(device)
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~~~
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~~~
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def speech_file_to_array_fn(path, sampling_rate):
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speech_array, _sampling_rate = torchaudio.load(path)
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resampler = torchaudio.transforms.Resample(_sampling_rate)
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speech = resampler(speech_array).squeeze().numpy()
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return speech
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def predict(path, sampling_rate):
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speech = speech_file_to_array_fn(path, sampling_rate)
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inputs = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
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inputs = {key: inputs[key].to(device) for key in inputs}
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with torch.no_grad():
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logits = model(**inputs).logits
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scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
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outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
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return outputs
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~~~
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# prediction
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~~~
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# path for a sample
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path = '/data/jtes_v1.1/wav/f01/ang/f01_ang_01.wav'
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outputs = predict(path, sampling_rate)
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~~~
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~~~
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[{'Emotion': 'anger', 'Score': '78.3%'},
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{'Emotion': 'disgust', 'Score': '11.7%'},
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{'Emotion': 'fear', 'Score': '5.4%'},
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{'Emotion': 'happiness', 'Score': '4.1%'},
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{'Emotion': 'sadness', 'Score': '0.5%'}]
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~~~
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## Evaluation
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The following tables summarize the scores obtained by model overall and per each class.
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| Emotions | precision | recall | f1-score | accuracy |
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|-----------|-----------|--------|----------|----------|
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| anger | 0.82 | 1.00 | 0.81 | |
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| disgust | 0.85 | 0.96 | 0.85 | |
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| fear | 0.78 | 0.88 | 0.80 | |
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| happiness | 0.84 | 0.71 | 0.78 | |
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| sadness | 0.86 | 1.00 | 0.79 | |
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| | | | Overall | 0.806 |
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##
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Colab Notebook
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https://colab.research.google.com/drive/1aPPb_ZVS5dlFVZySly8Q80a44La1XjJu?usp=sharing
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104
config.json
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config.json
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{
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"_name_or_path": "lighteternal/wav2vec2-large-xlsr-53-greek",
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"activation_dropout": 0.0,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2ForSpeechClassification"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 1,
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"classifier_proj_size": 256,
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"codevector_dim": 256,
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"contrastive_logits_temperature": 0.1,
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"conv_bias": true,
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_kernel": [
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10,
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3,
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3,
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3,
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3,
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2,
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2
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],
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"conv_stride": [
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5,
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2,
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2,
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2,
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2,
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2,
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2
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],
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"ctc_loss_reduction": "mean",
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"ctc_zero_infinity": true,
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"diversity_loss_weight": 0.1,
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"do_stable_layer_norm": true,
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"eos_token_id": 2,
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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"feat_proj_dropout": 0.0,
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.0,
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"finetuning_task": "wav2vec2_clf",
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"gradient_checkpointing": true,
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"hidden_act": "gelu",
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"hidden_dropout": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "anger",
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"1": "disgust",
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"2": "fear",
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"3": "happiness",
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"4": "sadness"
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"anger": 0,
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"disgust": 1,
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"fear": 2,
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"happiness": 3,
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"sadness": 4
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},
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.1,
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"mask_channel_length": 10,
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"mask_channel_min_space": 1,
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"mask_channel_other": 0.0,
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"mask_channel_prob": 0.0,
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"mask_channel_selection": "static",
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"mask_feature_length": 10,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_min_space": 1,
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"mask_time_other": 0.0,
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"mask_time_prob": 0.05,
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"mask_time_selection": "static",
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"model_type": "wav2vec2",
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"num_attention_heads": 16,
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"num_codevector_groups": 2,
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"num_codevectors_per_group": 320,
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"num_conv_pos_embedding_groups": 16,
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"num_conv_pos_embeddings": 128,
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"pad_token_id": 54,
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"pooling_mode": "mean",
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"problem_type": "single_label_classification",
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"proj_codevector_dim": 256,
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"torch_dtype": "float32",
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"transformers_version": "4.11.0.dev0",
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"use_weighted_layer_sum": false,
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"vocab_size": 55
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}
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BIN
pytorch_model.bin (Stored with Git LFS)
BIN
pytorch_model.bin (Stored with Git LFS)
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{
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"best_metric": null,
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"best_model_checkpoint": null,
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"epoch": 4.991735537190083,
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"global_step": 300,
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"is_hyper_param_search": false,
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"is_local_process_zero": true,
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"is_world_process_zero": true,
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"log_history": [
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{
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{
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"epoch": 1.66,
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"eval_accuracy": 0.6033057570457458,
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"eval_loss": 1.0739655494689941,
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"eval_runtime": 74.6016,
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"eval_samples_per_second": 1.622,
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"eval_steps_per_second": 0.416,
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"eval_accuracy": 0.8512396812438965,
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"eval_runtime": 71.6672,
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"eval_samples_per_second": 1.688,
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"eval_steps_per_second": 0.433,
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"train_steps_per_second": 0.063
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}
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