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--- |
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license: apache-2.0 |
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language: en |
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tags: |
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- generated_from_trainer |
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datasets: |
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- speech_commands |
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metrics: |
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- accuracy |
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model-index: |
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- name: wav2vec2-conformer-rel-pos-large-finetuned-speech-commands |
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results: |
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- task: |
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type: audio-classification |
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name: audio classification |
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dataset: |
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type: speech_commands |
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name: speech_commands |
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split: v0.02 |
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metrics: |
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- type: accuracy |
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value: 0.9724 |
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name: accuracy |
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--- |
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# wav2vec2-conformer-rel-pos-large-finetuned-speech-commands |
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### Model description |
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This model is a fine-tuned version of [facebook/wav2vec2-conformer-rel-pos-large](https://huggingface.co/facebook/wav2vec2-conformer-rel-pos-large) on the [speech_commands](https://huggingface.co/datasets/speech_commands) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5245 |
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- Accuracy: 0.9724 |
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#### Intended uses & limitations |
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The model can spot one of the following keywords: "Yes", "No", "Up", "Down", "Left", "Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine", "Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow", "Backward", "Forward", "Follow", "Learn", "Visual". |
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The repository includes sample files that I recorded (WAV, 16Khz sampling rate, mono). The simplest way to use the model is with the ```pipeline``` API: |
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``` |
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>>> from transformers import pipeline |
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>>> p = pipeline("audio-classification", model="juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands") |
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>>> p("up16k.wav") |
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[{'score': 0.7008192539215088, 'label': 'up'}, {'score': 0.04346614331007004, 'label': 'off'}, {'score': 0.029526518657803535, 'label': 'left'}, {'score': 0.02905120886862278, 'label': 'stop'}, {'score': 0.027142534032464027, 'label': 'on'}] |
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>>> p("stop16k.wav") |
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[{'score': 0.6969656944274902, 'label': 'stop'}, {'score': 0.03391443192958832, 'label': 'up'}, {'score': 0.027382319793105125, 'label': 'seven'}, {'score': 0.020835857838392258, 'label': 'five'}, {'score': 0.018051736056804657, 'label': 'down'}] |
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>>> p("marvin16k.wav") |
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[{'score': 0.5276530981063843, 'label': 'marvin'}, {'score': 0.04645705968141556, 'label': 'down'}, {'score': 0.038583893328905106, 'label': 'backward'}, {'score': 0.03578080236911774, 'label': 'wow'}, {'score': 0.03178196772933006, 'label': 'bird'}] |
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``` |
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You can also use them with the ```Auto```API: |
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``` |
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>>> import torch, librosa |
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>>> from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor |
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>>> feature_extractor = Wav2Vec2FeatureExtractor() |
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>>> model = AutoModelForAudioClassification.from_pretrained("juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands") |
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>>> audio, rate = librosa.load("up16k.wav", sr = 16000) |
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>>> inputs = feature_extractor(audio, sampling_rate=16000, return_tensors = "pt") |
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>>> logits = model(inputs['input_values']) |
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>>> logits |
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SequenceClassifierOutput(loss=None, logits=tensor([[-0.4635, -1.0112, 4.7935, 0.8528, 1.6265, 0.6456, 1.5423, 2.0132, |
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1.6103, 0.5847, -2.2526, 0.8839, 0.8163, -1.5655, -1.4160, -0.4196, |
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-0.1097, -1.8827, 0.6609, -0.2022, 0.0971, -0.6205, 0.4492, 0.0926, |
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-2.4848, 0.2630, -0.4584, -2.4327, -1.1654, 0.3897, -0.3374, -1.2418, |
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-0.1045, 0.2827, -1.5667, -0.0963]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None) |
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>>> classes = torch.softmax(logits.logits, dim=1) |
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>>> torch.set_printoptions(precision=3, sci_mode=False) |
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>>> classes |
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tensor([[ 0.004, 0.002, 0.701, 0.014, 0.030, 0.011, |
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0.027, 0.043, 0.029, 0.010, 0.001, 0.014, |
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0.013, 0.001, 0.001, 0.004, 0.005, 0.001, |
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0.011, 0.005, 0.006, 0.003, 0.009, 0.006, |
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0.000, 0.008, 0.004, 0.001, 0.002, 0.009, |
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0.004, 0.002, 0.005, 0.008, 0.001, 0.005]], |
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grad_fn=<SoftmaxBackward0>) |
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>>> top_class = torch.argmax(logits.logits, dim=1) |
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>>> top_class |
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tensor([2]) |
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>>> model.config.id2label[top_class.numpy()[0]] |
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'up' |
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``` |
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### Training and evaluation data |
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- subset: v0.02 |
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- full training set |
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- full validation set |
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### Training procedure |
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The model was fine-tuned on [Amazon SageMaker](https://aws.amazon.com/sagemaker), using an [ml.p3dn.24xlarge](https://aws.amazon.com/fr/ec2/instance-types/p3/) instance (8 NVIDIA V100 GPUs). Total training time for 10 epochs was 4.5 hours. |
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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: 256 |
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- eval_batch_size: 256 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 1024 |
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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: 10 |
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#### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.2901 | 1.0 | 83 | 2.0542 | 0.8875 | |
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| 1.8375 | 2.0 | 166 | 1.5610 | 0.9316 | |
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| 1.4957 | 3.0 | 249 | 1.1850 | 0.9558 | |
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| 1.1917 | 4.0 | 332 | 0.9159 | 0.9695 | |
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| 1.0449 | 5.0 | 415 | 0.7624 | 0.9687 | |
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| 0.9319 | 6.0 | 498 | 0.6444 | 0.9715 | |
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| 0.8559 | 7.0 | 581 | 0.5806 | 0.9711 | |
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| 0.8199 | 8.0 | 664 | 0.5394 | 0.9721 | |
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| 0.7949 | 9.0 | 747 | 0.5245 | 0.9724 | |
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| 0.7975 | 10.0 | 830 | 0.5256 | 0.9721 | |
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#### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu102 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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