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--- |
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license: apache-2.0 |
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base_model: |
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- facebook/wav2vec2-base |
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tags: |
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- intent-classification |
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- slu |
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- audio-classification |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: wav2vec2-base-fsc-gold |
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results: [] |
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datasets: |
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- fsc |
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language: |
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- en |
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pipeline_tag: audio-classification |
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library_name: transformers |
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--- |
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# wav2vec2-base-FSC-GOLD (Retain Set) |
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This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the FSC dataset (retain set) for the intent classification task. |
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It achieves the following results on the test set: |
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- Accuracy: 0.992 |
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- F1: 0.993 |
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## Model description |
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The base [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. |
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## Task and dataset description |
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Intent Classification (IC) classifies utterances into predefined classes to determine the intent of speakers. |
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The dataset used here is [Fluent Speech Commands (FSC)](https://arxiv.org/pdf/1904.03670), where each utterance is tagged with three intent labels: action, object, and location. |
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## Usage examples |
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You can use the model directly in the following manner: |
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```python |
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import torch |
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import librosa |
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from transformers import AutoModelForAudioClassification, AutoFeatureExtractor |
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## Load an audio file |
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audio_array, sr = librosa.load("path_to_audio.wav", sr=16000) |
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## Load model and feature extractor |
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model = AutoModelForAudioClassification.from_pretrained("alkiskoudounas/wav2vec2-base-fsc-gold") |
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feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base") |
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## Extract features |
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inputs = feature_extractor(audio_array.squeeze(), sampling_rate=feature_extractor.sampling_rate, padding=True, return_tensors="pt") |
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## Compute logits |
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logits = model(**inputs).logits |
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``` |
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## Framework versions |
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- Datasets 3.2.0 |
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- Pytorch 2.1.2 |
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- Tokenizers 0.20.3 |
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- Transformers 4.45.2 |
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## BibTeX entry and citation info |
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```bibtex |
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@inproceedings{koudounas2025unlearning, |
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title={"Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding}, |
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author={Koudounas, Alkis and Savelli, Claudio and Giobergia, Flavio and Baralis, Elena}, |
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booktitle={Proc. Interspeech 2025}, |
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year={2025}, |
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} |
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``` |
