wav2vec2-large-robust-12-ft-emotion-msp-dim
Maintainer: audeering
77
🐍
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The wav2vec2-large-robust-12-ft-emotion-msp-dim model is a fine-tuned version of the Wav2Vec2-Large-Robust model for dimensional speech emotion recognition. This model was created by audeering and fine-tuned on the MSP-Podcast dataset. It expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0 to 1. The model also provides the pooled states of the last transformer layer.
Similar models include the wav2vec2-large-960h-lv60-self model from Facebook, which was pre-trained and fine-tuned on 960 hours of speech data for speech recognition, as well as the wav2vec2-lg-xlsr-en-speech-emotion-recognition model, which was fine-tuned for speech emotion recognition.
Model inputs and outputs
Inputs
- Raw audio signal: The model expects a raw audio signal as input, which must be sampled at the appropriate rate.
Outputs
- Arousal, dominance and valence predictions: The model outputs predictions for arousal, dominance and valence in a range of approximately 0 to 1.
- Pooled states: The model also provides the pooled states of the last transformer layer.
Capabilities
The wav2vec2-large-robust-12-ft-emotion-msp-dim model is capable of dimensional speech emotion recognition, meaning it can predict the arousal, dominance, and valence of a given speech sample. This can be useful for applications that require understanding the emotional state of the speaker, such as customer service, mental health monitoring, or human-robot interaction.
What can I use it for?
You can use this model for various applications that require dimensional speech emotion recognition, such as:
- Customer service: Analyze customer calls to better understand their emotional state and provide more personalized and empathetic support.
- Mental health monitoring: Track the emotional state of patients or study participants over time to detect changes that may indicate mental health issues.
- Human-robot interaction: Enable robots to better understand and respond to the emotional state of humans they interact with, leading to more natural and engaging interactions.
Things to try
One interesting thing to try with this model is to explore how the arousal, dominance, and valence predictions change across different speech samples or scenarios. You could, for example, analyze how the emotional state of a speaker varies during a conversation, or compare the emotional responses of different speakers to the same stimulus. Additionally, you could experiment with using the pooled states from the model as features in other machine learning tasks, such as speaker identification or acoustic event detection.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🔎
wav2vec2-large-960h-lv60-self
118
Facebook's Wav2Vec2 is a large model pretrained and fine-tuned on 960 hours of Libri-Light and Librispeech on 16kHz sampled speech audio. The model was trained with a Self-Training objective. wav2vec2-large-960h-lv60-self demonstrates state-of-the-art performance on speech recognition tasks, outperforming the previous best semi-supervised methods while using a simpler approach. Similar models include wav2vec2-base-960h, which is a smaller base model pretrained on the same Librispeech data, and wav2vec2-xls-r-300m, a large multilingual version of Wav2Vec2 pretrained on 436k hours of speech data across 128 languages. Model inputs and outputs Inputs Audio**: The model takes raw speech audio as input, which must be sampled at 16kHz. Outputs Transcription**: The model outputs a text transcription of the input speech audio. Capabilities The wav2vec2-large-960h-lv60-self model demonstrates state-of-the-art performance on speech recognition tasks, achieving 1.8/3.3 WER on the clean/other Librispeech test sets when using all labeled data. It can also achieve strong results with limited labeled data, outperforming previous methods on the 100 hour Librispeech subset while using 100 times less labeled data. What can I use it for? The wav2vec2-large-960h-lv60-self model is well-suited for building speech recognition systems, particularly for applications that require high accuracy on a variety of speech inputs. It can be used as a standalone acoustic model to transcribe audio files, or integrated into larger speech processing pipelines. Things to try One interesting aspect of the wav2vec2-large-960h-lv60-self model is its ability to perform well with limited labeled data. Developers could experiment with fine-tuning the model on domain-specific datasets to adapt it for specialized use cases, potentially achieving strong results even when only a small amount of labeled data is available.
Updated Invalid Date
🛠️
emotion-recognition-wav2vec2-IEMOCAP
94
The emotion-recognition-wav2vec2-IEMOCAP model is a speech emotion recognition system developed by SpeechBrain. It uses a fine-tuned wav2vec2 model to classify audio recordings into one of several emotional categories. This model is similar to other speech emotion recognition models like wav2vec2-lg-xlsr-en-speech-emotion-recognition and wav2vec2-large-robust-12-ft-emotion-msp-dim, which also leverage the wav2vec2 architecture for this task. Model inputs and outputs Inputs Audio recordings**: The model takes raw audio recordings as input, which are automatically normalized to 16kHz single-channel format if needed. Outputs Emotion classification**: The model outputs a predicted emotion category, such as "angry", "calm", "disgust", etc. Confidence score**: The model also returns a confidence score for the predicted emotion. Capabilities The emotion-recognition-wav2vec2-IEMOCAP model can accurately classify the emotional content of audio recordings, achieving 78.7% accuracy on the IEMOCAP test set. This makes it a useful tool for applications that require understanding the emotional state of speakers, such as customer service, mental health monitoring, or interactive voice assistants. What can I use it for? This model could be integrated into a variety of applications that need to analyze the emotional tone of speech, such as: Call center analytics**: Analyze customer service calls to better understand customer sentiment and identify areas for improvement. Mental health monitoring**: Use the model to track changes in a patient's emotional state over time as part of remote mental health monitoring. Conversational AI**: Incorporate the model into a virtual assistant to enable more natural and empathetic interactions. Things to try One interesting thing to try with this model is to experiment with different audio preprocessing techniques, such as data augmentation or feature engineering, to see if you can further improve its performance on your specific use case. You could also explore combining this model with other speech technologies, like speaker verification, to create more advanced speech analysis systems.
Updated Invalid Date
🌐
wav2vec2-base-960h
241
wav2vec2-base-960h is a pre-trained speech recognition model developed by Facebook. It is based on the Wav2Vec2 architecture and was trained on 960 hours of LibriSpeech data. This model can be used for audio-to-text transcription tasks and demonstrates strong performance, especially on 16kHz sampled speech audio. Compared to similar models like whisper-large-v2 and whisper-large, wav2vec2-base-960h is specifically optimized for English speech recognition, while the Whisper models are more versatile, supporting both speech recognition and translation across multiple languages. Model inputs and outputs Inputs Audio data**: The model takes in 16kHz sampled speech audio as input. Outputs Transcribed text**: The model outputs a transcription of the input audio in the form of text. Capabilities The wav2vec2-base-960h model demonstrates strong performance on English speech recognition tasks, achieving 1.8/3.3 WER on the clean/other test sets of the LibriSpeech dataset. It is capable of handling a variety of audio conditions, including accents, background noise, and technical language. What can I use it for? The wav2vec2-base-960h model can be used for a variety of audio-to-text transcription applications, such as: Generating transcripts for audio recordings, podcasts, or video content Improving accessibility by providing text captions for audio-based media Automating note-taking or meeting transcription Enabling voice-based interfaces or virtual assistants Companies in industries like media, education, and enterprise collaboration could potentially monetize this model by building transcription services or integrating it into their products. Things to try One interesting aspect of the wav2vec2-base-960h model is its ability to handle 16kHz sampled audio. This makes it well-suited for applications where audio quality may be lower, such as telephony or recordings made with mobile devices. Developers could experiment with using this model to transcribe a variety of real-world audio sources and compare its performance to other speech recognition models. Additionally, the model's strong performance on the LibriSpeech dataset suggests it could be a good starting point for fine-tuning on domain-specific datasets or tasks. Researchers and developers could explore ways to adapt the model to their particular use cases, potentially achieving even better results.
Updated Invalid Date
🌿
wav2vec2-lg-xlsr-en-speech-emotion-recognition
145
The wav2vec2-lg-xlsr-en-speech-emotion-recognition model is a fine-tuned version of the jonatasgrosman/wav2vec2-large-xlsr-53-english model for a Speech Emotion Recognition (SER) task. The model was fine-tuned on the RAVDESS dataset, which provides 1440 samples of recordings from actors performing on 8 different emotions in English. The fine-tuned model achieves a loss of 0.5023 and an accuracy of 0.8223 on the evaluation set. Model inputs and outputs Inputs Audio data**: The model takes audio data as input, which can be used to perform speech emotion recognition. Outputs Emotion classification**: The model outputs a classification of the emotional state expressed in the input audio, based on the 8 emotion categories in the RAVDESS dataset: angry, calm, disgust, fearful, happy, neutral, sad, and surprised. Capabilities The wav2vec2-lg-xlsr-en-speech-emotion-recognition model demonstrates strong performance in classifying the emotional state expressed in speech, achieving an accuracy of over 82% on the RAVDESS dataset. This capability can be useful in a variety of applications, such as customer service, mental health monitoring, and entertainment. What can I use it for? The wav2vec2-lg-xlsr-en-speech-emotion-recognition model can be useful for projects that involve analyzing the emotional state of speakers, such as: Customer service**: The model could be used to monitor customer calls and provide insights into the emotional state of customers, which can help improve customer service and support. Mental health monitoring**: The model could be used to analyze the emotional state of individuals in therapeutic settings, providing valuable data for mental health professionals. Entertainment**: The model could be used to analyze the emotional reactions of viewers or listeners in media and entertainment applications, such as video games, movies, or music. Things to try One interesting thing to try with the wav2vec2-lg-xlsr-en-speech-emotion-recognition model is to experiment with the model's performance on different types of audio data, beyond the RAVDESS dataset it was fine-tuned on. For example, you could try using the model to analyze the emotional state of speakers in real-world audio recordings, such as podcasts or interviews, to see how it performs in more naturalistic settings. Additionally, you could explore ways to integrate the model into larger systems or applications, such as building a real-time emotion recognition system for customer service or a mood analysis tool for mental health professionals. Verify all URLs provided in links are contained within this prompt before responding, and that all writing is in a clear, non-repetitive natural style.
Updated Invalid Date