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--- |
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library_name: transformers |
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tags: |
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- flattery |
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- business calls |
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- speech |
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language: |
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- en |
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pipeline_tag: audio-classification |
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inference: false |
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--- |
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# Flattery Prediction from Speech |
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<!-- Provide a quick summary of what the model is/does. --> |
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This Wav2Vec2 model was finetuned to predict **flattery from speech** English **earning calls**. It was introduced in [This Paper Had the Smartest Reviewers -- Flattery Detection Utilising an |
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Audio-Textual Transformer-Based Approach](http://arxiv.org/abs/2406.17667), which was accepted at INTERSPEECH 2024. |
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If you are looking for the text-based classifier (based on RoBERTa) introduced in the paper, please see [here](https://huggingface.co/chrlukas/flattery_prediction_text). |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is a (further) fine-tuned variant of a [Wav2Vec2 model for Speech Emotion Recognition in MSP](https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim). It is trained using a dataset comprising single sentences uttered in business calls, |
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which were labeled for flattery in a binary manner. The training set comprised 7167 sentences, 1878 sentences were used as development set. For more details, please |
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refer to [the paper(TODO)](#), especially Sections 2 for the dataset, 3.2.2 for the training procedure and 4.2 for the results. The checkpoint provided here was trained without further pruning the model. |
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It achieves Unweighed Average Recall (UAR) values of .8001 and .8084 on the development and test partition, respectively. |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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## Usage |
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The following snippet illustrates the usage of the model. |
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```python |
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from transformers import AutoFeatureExtractor, Wav2Vec2ForSequenceClassification |
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from torch import sigmoid |
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import librosa |
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# initialize model and tokenizer |
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checkpoint = "chrlukas/flattery_prediction_speech" |
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processor = AutoFeatureExtractor.from_pretrained(checkpoint) |
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model = Wav2Vec2ForSequenceClassification.from_pretrained(checkpoint) |
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model.eval() |
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# predict flattery in a sentence |
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example_file = 'example.wav' |
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# audio must be resampled to 16Hz |
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y, _ = librosa.load(test_file, sr=16000) |
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inp = processor(y, sampling_rate=16000, return_tensors='pt') |
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with torch.no_grad(): |
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logits = model(**inp).logits |
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prediction = sigmoid(logits).item() |
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flattery = prediction >= 0.5 |
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print(f'Flattery detected? {flattery}') |
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``` |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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The model is trained on a highly-domain specific dataset sourced from earning calls, i.e., typically conversations between business analysts and CEOs of US-American companies. Hence, it can not be expected to generalize well to other |
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domains and contexts. Moreover, the majority of speakers (162/178) in the training dataset are male. However, we found this to have rather little impact on the model's performance for |
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held-out female speakers (cf. Section 4.4 in the paper) |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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TODO |
