README.md

---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: my_awesome_mind_model
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: minds14
      type: minds14
      config: en-US
      split: train
      args: en-US
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.061946902654867256
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# my_awesome_mind_model

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6577
- Accuracy: 0.0619

## Model description

Base Model used for fne tuning https://huggingface.co/facebook/wav2vec2-base

## Intended uses & limitations

Use it to familarize yourself with fine tuning a pre trained model.Not a Production Ready Model

## Training and evaluation data

This is the link to the training dataset. https://huggingface.co/datasets/PolyAI/minds14
You can bring your own data, preprocess it for training on your own data.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.8   | 3    | 2.6463          | 0.0619   |
| No log        | 1.8   | 6    | 2.6525          | 0.0442   |
| No log        | 2.8   | 9    | 2.6524          | 0.0619   |
| 3.0286        | 3.8   | 12   | 2.6569          | 0.0619   |
| 3.0286        | 4.8   | 15   | 2.6572          | 0.0531   |
| 3.0286        | 5.8   | 18   | 2.6546          | 0.0619   |
| 3.0109        | 6.8   | 21   | 2.6593          | 0.0708   |
| 3.0109        | 7.8   | 24   | 2.6585          | 0.0531   |
| 3.0109        | 8.8   | 27   | 2.6569          | 0.0619   |
| 3.0047        | 9.8   | 30   | 2.6577          | 0.0619   |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0

### Inference - How to Use

Load an audio file you'd like to run inference on. Remember to resample the sampling rate of the audio file to match the sampling rate of the model if you need to!



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# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
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Audio classification

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#@title
from IPython.display import HTML

HTML('<iframe width="560" height="315" src="https://www.youtube.com/embed/KWwzcmG98Ds?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')

Audio classification - just like with text - assigns a class label output from the input data. The only difference is instead of text inputs, you have raw audio waveforms. Some practical applications of audio classification include identifying speaker intent, language classification, and even animal species by their sounds.

This guide will show you how to:

Finetune Wav2Vec2 on the MInDS-14 dataset to classify speaker intent.
Use your finetuned model for inference.
The task illustrated in this tutorial is supported by the following model architectures:
Audio Spectrogram Transformer, Data2VecAudio, Hubert, SEW, SEW-D, UniSpeech, UniSpeechSat, Wav2Vec2, Wav2Vec2-Conformer, WavLM, Whisper

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:


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!pip install transformers datasets evaluate
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from huggingface_hub import notebook_login

notebook_login()

Load MInDS-14 dataset
Start by loading the MInDS-14 dataset from the 🤗 Datasets library:


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from datasets import load_dataset, Audio

minds = load_dataset("PolyAI/minds14", name="en-US", split="train")

Split the dataset's train split into a smaller train and test set with the train_test_split method. This'll give you a chance to experiment and make sure everything works before spending more time on the full dataset.


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minds = minds.train_test_split(test_size=0.2)
Then take a look at the dataset:


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minds
DatasetDict({
    train: Dataset({
        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
        num_rows: 450
    })
    test: Dataset({
        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
        num_rows: 113
    })
})
While the dataset contains a lot of useful information, like lang_id and english_transcription, you'll focus on the audio and intent_class in this guide. Remove the other columns with the remove_columns method:


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minds = minds.remove_columns(["path", "transcription", "english_transcription", "lang_id"])
Take a look at an example now:


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minds["train"][0]
{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f9018fd3747971e77d59e6c5da3fdf9d5bb914c495e16c23e1fe47c921d76a7a/en-US~HIGH_VALUE_PAYMENT/602b9d96963e11ccd901cc18.wav',
  'array': array([ 0.        ,  0.        ,  0.        , ...,  0.        ,
          0.00024414, -0.00024414]),
  'sampling_rate': 8000},
 'intent_class': 10}
There are two fields:

audio: a 1-dimensional array of the speech signal that must be called to load and resample the audio file.
intent_class: represents the class id of the speaker's intent.
To make it easier for the model to get the label name from the label id, create a dictionary that maps the label name to an integer and vice versa:


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labels = minds["train"].features["intent_class"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
    label2id[label] = str(i)
    id2label[str(i)] = label
Now you can convert the label id to a label name:


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id2label[str(2)]

Preprocess
The next step is to load a Wav2Vec2 feature extractor to process the audio signal:


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from transformers import AutoFeatureExtractor

feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")

The MInDS-14 dataset has a sampling rate of 8000khz (you can find this information in it's dataset card), which means you'll need to resample the dataset to 16000kHz to use the pretrained Wav2Vec2 model:


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minds = minds.cast_column("audio", Audio(sampling_rate=16_000))
minds["train"][0]
{'audio': {'path': '/root/.cache/huggingface/datasets/downloads/extracted/f9018fd3747971e77d59e6c5da3fdf9d5bb914c495e16c23e1fe47c921d76a7a/en-US~HIGH_VALUE_PAYMENT/602b9d96963e11ccd901cc18.wav',
  'array': array([ 5.25035284e-06,  1.37879761e-05, -5.62806963e-06, ...,
         -1.10931869e-05, -2.25960350e-04, -1.97247806e-04]),
  'sampling_rate': 16000},
 'intent_class': 10}
Now create a preprocessing function that:

Calls the audio column to load, and if necessary, resample the audio file.
Checks if the sampling rate of the audio file matches the sampling rate of the audio data a model was pretrained with. You can find this information in the Wav2Vec2 model card.
Set a maximum input length to batch longer inputs without truncating them.

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def preprocess_function(examples):
    audio_arrays = [x["array"] for x in examples["audio"]]
    inputs = feature_extractor(
        audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=16000, truncation=True
    )
    return inputs
To apply the preprocessing function over the entire dataset, use 🤗 Datasets map function. You can speed up map by setting batched=True to process multiple elements of the dataset at once. Remove the columns you don't need, and rename intent_class to label because that's the name the model expects:


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encoded_minds = minds.map(preprocess_function, remove_columns="audio", batched=True)
encoded_minds = encoded_minds.rename_column("intent_class", "label")

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the accuracy metric (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric):


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import evaluate

accuracy = evaluate.load("accuracy")

Then create a function that passes your predictions and labels to compute to calculate the accuracy:


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import numpy as np


def compute_metrics(eval_pred):
    predictions = np.argmax(eval_pred.predictions, axis=1)
    return accuracy.compute(predictions=predictions, references=eval_pred.label_ids)
Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.

Train
If you aren't familiar with finetuning a model with the Trainer, take a look at the basic tutorial here!

You're ready to start training your model now! Load Wav2Vec2 with AutoModelForAudioClassification along with the number of expected labels, and the label mappings:


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from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer

num_labels = len(id2label)
model = AutoModelForAudioClassification.from_pretrained(
    "facebook/wav2vec2-base", num_labels=num_labels, label2id=label2id, id2label=id2label
)

At this point, only three steps remain:

Define your training hyperparameters in TrainingArguments. The only required parameter is output_dir which specifies where to save your model. You'll push this model to the Hub by setting push_to_hub=True (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the Trainer will evaluate the accuracy and save the training checkpoint.
Pass the training arguments to Trainer along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call train() to finetune your model.

[18]
1h
training_args = TrainingArguments(
    output_dir="my_awesome_mind_model",
    evaluation_strategy="epoch",
    save_strategy="epoch",
    learning_rate=3e-5,
    per_device_train_batch_size=32,
    gradient_accumulation_steps=4,
    per_device_eval_batch_size=32,
    num_train_epochs=10,
    warmup_ratio=0.1,
    logging_steps=10,
    load_best_model_at_end=True,
    metric_for_best_model="accuracy",
    push_to_hub=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=encoded_minds["train"],
    eval_dataset=encoded_minds["test"],
    tokenizer=feature_extractor,
    compute_metrics=compute_metrics,
)

trainer.train()

Once training is completed, share your model to the Hub with the push_to_hub() method so everyone can use your model:


[19]
10s
trainer.push_to_hub()

For a more in-depth example of how to finetune a model for audio classification, take a look at the corresponding PyTorch notebook.

Inference
Great, now that you've finetuned a model, you can use it for inference!

Load an audio file you'd like to run inference on. Remember to resample the sampling rate of the audio file to match the sampling rate of the model if you need to!

The simplest way to try out this finetuned model for inference is to use it in a pipeline(). Instantiate a pipeline for audio classification with your model, and pass your audio file to it: