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

Model description

This model is a fine-tuned version of facebook/wav2vec2-conformer-rel-pos-large on the 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 with the AutoAPI:

>>> 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, using an ml.p3dn.24xlarge 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

Framework versions

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