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--- |
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license: apache-2.0 |
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language: |
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- sl |
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- hr |
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- sr |
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base_model: |
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- facebook/w2v-bert-2.0 |
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pipeline_tag: audio-classification |
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metrics: |
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- f1 |
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--- |
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# Frame classification for filled pauses |
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This model classifies individual 20ms frames of audio based on presence of filled pauses ("eee", "errm", ...). |
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It was trained on human-annotated Slovenian speech corpus ROG-Artur and achieves F1 of 0.95 for the positive class on |
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te test split of the same dataset. |
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# Evaluation |
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Although the output of the model is a series 0 or 1, describing their 20ms frames, the evaluation was done on |
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event level; spans of consecutive outputs 1 were bundled together into one event. When the true and predicted |
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events partially overlap, this is counted as a true positive. |
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## Evaluation on ROG corpus |
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In evaluation, we only evaluate positive events, i.e. |
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``` |
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precision recall f1-score support |
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1 0.907 0.987 0.946 1834 |
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``` |
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## Evaluation on ParlaSpeech [HR](https://huggingface.co/datasets/classla/ParlaSpeech-HR) and [RS](https://huggingface.co/datasets/classla/ParlaSpeech-RS) corpora |
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Evaluation on 800 human-annotated instances ParlaSpeech-HR and ParlaSpeech-RS produced the following metrics: |
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``` |
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Performance on RS: |
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Classification report for human vs model on event level: |
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precision recall f1-score support |
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1 0.95 0.99 0.97 542 |
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Performance on HR: |
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Classification report for human vs model on event level: |
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precision recall f1-score support |
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1 0.93 0.98 0.95 531 |
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``` |
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The metrics reported are on event level, which means that if true and |
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predicted filled pauses at least partially overlap, we count them as a |
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True Positive event. |
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# Example use: |
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```python |
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from transformers import AutoFeatureExtractor, Wav2Vec2BertForAudioFrameClassification |
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from datasets import Dataset, Audio |
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import torch |
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import numpy as np |
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from pathlib import Path |
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device = torch.device("cuda") |
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model_name = "classla/wav2vecbert2-filledPause" |
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) |
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model = Wav2Vec2BertForAudioFrameClassification.from_pretrained(model_name).to(device) |
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ds = Dataset.from_dict( |
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{ |
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"audio": [ |
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"/cache/peterr/mezzanine_resources/filled_pauses/data/dev/Iriss-J-Gvecg-P500001-avd_2082.293_2112.194.wav" |
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], |
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} |
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).cast_column("audio", Audio(sampling_rate=16_000, mono=True)) |
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def evaluator(chunks): |
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sampling_rate = chunks["audio"][0]["sampling_rate"] |
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with torch.no_grad(): |
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inputs = feature_extractor( |
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[i["array"] for i in chunks["audio"]], |
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return_tensors="pt", |
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sampling_rate=sampling_rate, |
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).to(device) |
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logits = model(**inputs).logits |
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y_pred = np.array(logits.cpu()).argmax(axis=-1) |
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return {"y_pred": y_pred.tolist()} |
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ds = ds.map(evaluator, batched=True) |
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print(ds["y_pred"][0]) |
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# Returns a list of 20ms frames: [0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,....] |
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# with 0 indicating no filled pause detected in that frame |
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``` |
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# Citation |
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Coming soon. |
