Create README.md (#1)

- Create README.md (3b729260f439065cf36e4c966c7a9935c641709c)


Co-authored-by: Tomás Bernal Beltrán <tomasBernal@users.noreply.huggingface.co>

README.md

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+---
+language:
+- en
+license: mit
+library_name: transformers
+pipeline_tag: audio-classification
+tags:
+- emotion-recognition
+- speech-emotion-recognition
+- audio-classification
+- speech-processing
+- english
+- affective-computing
+- umuteam
+datasets:
+- RAVDESS
+- TESS
+metrics:
+- accuracy
+- f1
+
+model-index:
+- name: UMUTeam/w2v-bert-emotion-en
+  results:
+  - task:
+      type: audio-classification
+      name: Speech Emotion Recognition
+    dataset:
+      name: English Speech Emotion Recognition Benchmark
+      type: custom
+    metrics:
+    - type: accuracy
+      value: 95.1435
+      name: Accuracy
+    - type: weighted-f1
+      value: 95.1575
+      name: Weighted F1
+    - type: macro-f1
+      value: 95.1679
+      name: Macro F1
+---
+
+# UMUTeam/w2v-bert-emotion-en
+
+## Model description
+
+`UMUTeam/w2v-bert-emotion-en` is an English speech emotion recognition model developed as part of **speech-emotion**, an open-source multilingual and multimodal toolkit for emotion recognition from speech, text, and multimodal inputs.
+
+This model performs **emotion classification directly from English speech audio**.
+
+The model is based on the Wav2Vec2-BERT architecture and was fine-tuned for speech emotion recognition tasks in English.
+
+It is designed to operate as a standalone speech-only emotion recognition system or as part of the broader `speech-emotion` framework, where acoustic representations can be combined with textual representations for multimodal emotion recognition.
+
+The model predicts one of the following emotion labels:
+
+- `angry`
+- `disgust`
+- `fear`
+- `happy`
+- `neutral`
+- `sad`
+- `surprise`
+
+## Intended use
+
+This model is intended for research and applied scenarios involving English speech emotion recognition, such as:
+
+- emotion analysis from speech recordings
+- conversational speech analysis
+- affective computing research
+- human-computer interaction
+- emotion-aware conversational agents
+- integration into multimodal emotion recognition pipelines
+
+It can be used directly with the Hugging Face `transformers` library or through the `speech-emotion` toolkit.
+
+## Out-of-scope use
+
+This model should not be used as the sole basis for high-stakes decisions, including but not limited to:
+
+- clinical diagnosis
+- mental health assessment
+- employment, legal, or educational decisions
+- biometric profiling or surveillance
+- automated decisions affecting individuals without human oversight
+
+Emotion recognition is inherently uncertain and context-dependent. Predictions should be interpreted as model estimates, not as definitive assessments of a person's emotional state.
+
+## Training data
+
+The model was trained on the English speech datasets used in the `speech-emotion` project.
+
+The training data combines multiple publicly available English speech emotion recognition datasets, including:
+
+- RAVDESS
+- TESS
+- datasets derived from prior speech emotion recognition research benchmarks
+
+Because the original datasets use different emotion taxonomies, all datasets were harmonized into a unified seven-class emotion taxonomy:
+
+- `angry`
+- `disgust`
+- `fear`
+- `happy`
+- `neutral`
+- `sad`
+- `surprise`
+
+For the English speech emotion recognition setup:
+
+- Training samples: 3,622
+- Validation samples: 453
+- Test samples: 453
+
+More details about the dataset preprocessing and label harmonization pipeline are available in the project repository:
+
+https://github.com/NLP-UMUTeam/umuteam-speech-emotion
+
+## Evaluation
+
+The model was evaluated on the English held-out test set used in the `speech-emotion` toolkit.
+
+### Performance comparison on English emotion recognition
+
+| Configuration | Accuracy | Weighted Precision | Weighted F1 | Macro F1 |
+|---|---:|---:|---:|---:|
+| Speech-only | 95.1435 | 95.2700 | 95.1575 | 95.1679 |
+| Text-only | 76.0842 | 75.5723 | 75.6852 | 68.0266 |
+| Multimodal (Concat) | **96.0462** | **96.0880** | **96.0257** | **96.0462** |
+| Multimodal (Mean) | 90.2870 | 90.5162 | 90.2334 | 90.2589 |
+| Multimodal (Multihead) | 93.1567 | 93.2715 | 93.1898 | 93.2115 |
+
+These results show that speech-based emotion recognition provides strong performance for English emotion analysis, while multimodal approaches combining speech and text achieve even higher robustness and overall performance.
+
+## How to use
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "audio-classification",
+    model="UMUTeam/w2v-bert-emotion-en"
+)
+
+prediction = classifier("audio.wav")
+
+print(prediction)
+```
+
+You can also use this model through the `speech-emotion` toolkit:
+
+```bash
+pip install speech-emotion
+```
+
+```python
+from speech_emotion import predict_emotion
+
+emotion = predict_emotion(
+    audio_path="audio.wav",
+    language="en",
+    mode="audio",
+    model_config_path="model.json"
+)
+
+print("Detected emotion:", emotion)
+```
+
+Repository:
+
+https://github.com/NLP-UMUTeam/umuteam-speech-emotion
+
+## Limitations
+
+- The model is designed for English speech and may not perform reliably on other languages.
+- It predicts a single label from a fixed set of seven emotions.
+- Emotion expression is subjective and highly context-dependent.
+- Performance may decrease with noisy audio, overlapping speakers, low-quality recordings, strong accents, or domain shifts.
+- Speech-only emotion recognition may miss relevant contextual or visual information that could improve emotion interpretation.
+
+## Bias and ethical considerations
+
+Emotion recognition systems may reflect biases present in their training data, including differences related to accents, speaking styles, demographics, recording conditions, or annotation subjectivity.
+
+Users should avoid interpreting predictions as objective truths about a person's internal emotional state. The model should be used with transparency, appropriate consent, and human oversight, especially in sensitive contexts.
+
+## Citation
+
+If you use this model in your research, please cite the following works:
+
+### speech-emotion toolkit
+
+```bibtex
+@article{PAN2026102677,
+title = {speech-emotion: A multilingual and multimodal toolkit for emotion recognition from speech},
+journal = {SoftwareX},
+volume = {34},
+pages = {102677},
+year = {2026},
+issn = {2352-7110},
+doi = {https://doi.org/10.1016/j.softx.2026.102677},
+url = {https://www.sciencedirect.com/science/article/pii/S235271102600169X},
+author = {Ronghao Pan and Tomás Bernal-Beltrán and José Antonio García-Díaz and Rafael Valencia-García},
+}
+```
+
+## Acknowledgments
+
+This work is part of the research project LaTe4PoliticES (PID2022-138099OB-I00), funded by MICIU/AEI/10.13039/501100011033 and the European Regional Development Fund (ERDF/EU - FEDER/UE), “A way of making Europe”.
+
+Mr. Tomás Bernal-Beltrán is supported by the University of Murcia through the predoctoral programme.