pytorch wav2vec2-conformer audio-classification en dataset:speech_commands transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
language: en
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: wav2vec2-conformer-rel-pos-large-finetuned-speech-commands
  results:
  - task:
       type: audio-classification
       name: audio classification
    dataset: 
      type: speech_commands
      name: speech_commands
      split: v0.02
    metrics:
    - type: accuracy
      value: 0.9724
      name: accuracy
---

# wav2vec2-conformer-rel-pos-large-finetuned-speech-commands

### Model description

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.

It achieves the following results on the evaluation set:
- Loss: 0.5245
- Accuracy: 0.9724

#### Intended uses & limitations

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".

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:

```
>>> from transformers import pipeline
>>> p = pipeline("audio-classification", model="juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands")
>>> p("up16k.wav")
[{'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'}]
>>> p("stop16k.wav")
[{'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'}]
>>> p("marvin16k.wav")
[{'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'}]
```

You can also use them model with the ```Auto```API:

```
>>> import torch, librosa
>>> from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor
>>> feature_extractor = Wav2Vec2FeatureExtractor()
>>> model = AutoModelForAudioClassification.from_pretrained("juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands")
>>> audio, rate = librosa.load("up16k.wav", sr = 16000)
>>> inputs = feature_extractor(audio, sampling_rate=16000, return_tensors = "pt")
>>> logits = model(inputs['input_values'])
>>> logits
SequenceClassifierOutput(loss=None, logits=tensor([[-0.4635, -1.0112,  4.7935,  0.8528,  1.6265,  0.6456,  1.5423,  2.0132,
          1.6103,  0.5847, -2.2526,  0.8839,  0.8163, -1.5655, -1.4160, -0.4196,
         -0.1097, -1.8827,  0.6609, -0.2022,  0.0971, -0.6205,  0.4492,  0.0926,
         -2.4848,  0.2630, -0.4584, -2.4327, -1.1654,  0.3897, -0.3374, -1.2418,
         -0.1045,  0.2827, -1.5667, -0.0963]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)
>>> classes = torch.softmax(logits.logits, dim=1)
>>> torch.set_printoptions(precision=3, sci_mode=False)
>>> classes
tensor([[    0.004,     0.002,     0.701,     0.014,     0.030,     0.011,
             0.027,     0.043,     0.029,     0.010,     0.001,     0.014,
             0.013,     0.001,     0.001,     0.004,     0.005,     0.001,
             0.011,     0.005,     0.006,     0.003,     0.009,     0.006,
             0.000,     0.008,     0.004,     0.001,     0.002,     0.009,
             0.004,     0.002,     0.005,     0.008,     0.001,     0.005]],
       grad_fn=<SoftmaxBackward0>)
>>> top_class = torch.argmax(logits.logits, dim=1)
>>> top_class
tensor([2])
>>> model.config.id2label[top_class.numpy()[0]]
'up'
```

### Training and evaluation data

- subset: v0.02
- full training set
- full validation set

### Training procedure

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.

#### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

#### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2901        | 1.0   | 83   | 2.0542          | 0.8875   |
| 1.8375        | 2.0   | 166  | 1.5610          | 0.9316   |
| 1.4957        | 3.0   | 249  | 1.1850          | 0.9558   |
| 1.1917        | 4.0   | 332  | 0.9159          | 0.9695   |
| 1.0449        | 5.0   | 415  | 0.7624          | 0.9687   |
| 0.9319        | 6.0   | 498  | 0.6444          | 0.9715   |
| 0.8559        | 7.0   | 581  | 0.5806          | 0.9711   |
| 0.8199        | 8.0   | 664  | 0.5394          | 0.9721   |
| 0.7949        | 9.0   | 747  | 0.5245          | 0.9724   |
| 0.7975        | 10.0  | 830  | 0.5256          | 0.9721   |


#### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
