xeus
Maintainer: espnet
97
🤔
| 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
XEUS is a large-scale multilingual speech encoder developed by the WAVLab at Carnegie Mellon University. It covers over 4,000 languages and is pre-trained on over 1 million hours of publicly available speech datasets. XEUS uses the E-Branchformer architecture and is trained using HuBERT-style masked prediction of discrete speech tokens extracted from WavLabLM. The total model size is 577M parameters.
XEUS tops the ML-SUPERB multilingual speech recognition leaderboard, outperforming models like MMS, w2v-BERT 2.0, and XLS-R. It also sets a new state-of-the-art on 4 tasks in the monolingual SUPERB benchmark.
Model inputs and outputs
Inputs
- Audio Waveform:
XEUStakes raw audio waveform as input, which it encodes into a sequence of speech representations.
Outputs
- Speech Representations: The model outputs a sequence of speech representations that can be used for downstream tasks such as speech recognition or translation. These representations capture the semantic and acoustic properties of the input speech.
Capabilities
XEUS is a powerful multilingual speech encoder that can be leveraged for a variety of speech-related tasks. Its broad language coverage and robust performance on benchmarks make it a compelling choice for those working on multilingual speech applications.
What can I use it for?
XEUS can be used as a speech encoder in various downstream applications, such as automatic speech recognition, speech-to-text translation, and speech-based semantic understanding. By fine-tuning the model on task-specific data, users can take advantage of its strong multilingual capabilities to build solutions that work across a wide range of languages.
Things to try
One interesting aspect of XEUS is its use of the E-Branchformer architecture and HuBERT-style training. This allows the model to learn robust speech representations that capture both semantic and acoustic properties of the input. When fine-tuning XEUS on downstream tasks, it would be interesting to explore how the model's performance compares to other multilingual speech models and how the architectural choices impact the final results.
Another area to explore is the model's ability to handle low-resource languages. With its coverage of over 4,000 languages, XEUS could be a valuable tool for building speech technologies for endangered or under-resourced languages. Researchers and developers could investigate the model's performance on these languages and explore techniques for further improving its capabilities in low-resource settings.
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-xlsr-53
86
wav2vec2-large-xlsr-53 is a pre-trained speech recognition model developed by Facebook. It is a large-scale multilingual model that can be fine-tuned on specific languages and tasks. The model was pre-trained on 16kHz sampled speech audio from 53 languages, leveraging the wav2vec 2.0 objective which learns powerful representations from raw speech audio alone. Fine-tuning this model on labeled data can significantly outperform previous state-of-the-art results, even when using limited amounts of labeled data. Similar models include Wav2Vec2-XLS-R-300M, a 300 million parameter version, and fine-tuned models like wav2vec2-large-xlsr-53-english and wav2vec2-large-xlsr-53-chinese-zh-cn created by Jonatas Grosman. Model inputs and outputs Inputs Audio data**: The model takes in raw 16kHz sampled speech audio as input. Outputs Text transcription**: The model outputs a text transcription of the input speech audio. Capabilities The wav2vec2-large-xlsr-53 model demonstrates impressive cross-lingual speech recognition capabilities, leveraging the shared latent representations learned during pre-training to perform well across a wide range of languages. On the CommonVoice benchmark, the model shows a 72% relative reduction in phoneme error rate compared to previous best results. It also improves word error rate by 16% relative on the BABEL dataset compared to prior systems. What can I use it for? This model can be used as a powerful foundation for building speech recognition systems in a variety of languages. By fine-tuning the model on labeled data in a target language, you can create highly accurate speech-to-text transcription models, even with limited labeled data. The cross-lingual nature of the pre-training also makes it well-suited for multilingual speech recognition applications. Some potential use cases include voice search, audio transcription, voice interfaces for applications, and speech translation. Companies in industries like media, healthcare, education, and customer service could potentially leverage this model to automate and improve their audio processing and understanding capabilities. Things to try An interesting avenue to explore would be combining this large-scale pre-trained model with language models or other specialized components to create more advanced speech processing pipelines. For example, integrating the acoustic model with a language model could potentially further improve transcription accuracy, especially for languages with complex grammar and vocabulary. Another interesting direction would be to investigate the model's few-shot or zero-shot learning capabilities - how well can it adapt to new languages or domains with minimal fine-tuning data? Pushing the boundaries of the model's cross-lingual and low-resource learning abilities could lead to exciting breakthroughs in democratizing speech technology.
Updated Invalid Date
👁️
wav2vec2-large-xlsr-53-english
423
The wav2vec2-large-xlsr-53-english model is a fine-tuned version of the facebook/wav2vec2-large-xlsr-53 model for speech recognition in English. It was fine-tuned on the train and validation splits of the Common Voice 6.1 dataset. This model can be used directly for speech recognition without the need for an additional language model. Similar models include the wav2vec2-large-xlsr-53-chinese-zh-cn model, which is fine-tuned for speech recognition in Chinese, and the wav2vec2-lg-xlsr-en-speech-emotion-recognition model, which is fine-tuned for speech emotion recognition in English. Model inputs and outputs Inputs Audio data**: The model expects audio input sampled at 16kHz. Outputs Text transcription**: The model outputs a text transcription of the input audio. Capabilities The wav2vec2-large-xlsr-53-english model can be used for accurate speech recognition in English. It was fine-tuned on a large and diverse dataset, allowing it to perform well on a wide range of speech content. What can I use it for? You can use this model to transcribe English audio files, such as recordings of meetings, interviews, or lectures. The model could be integrated into applications like voice assistants, subtitling tools, or automatic captioning systems. It could also be used as a starting point for further fine-tuning on domain-specific data to improve performance in specialized use cases. Things to try Try using the model with different types of English audio, such as conversational speech, read text, or specialized vocabulary. Experiment with different preprocessing steps, such as audio normalization or voice activity detection, to see if they improve the model's performance. You could also try combining the model with a language model to further improve the transcription accuracy.
Updated Invalid Date
🤷
xlm-roberta-large
280
The xlm-roberta-large model is a large-sized multilingual version of the RoBERTa model, developed and released by FacebookAI. It was pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages, as introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale. This model is a larger version of the xlm-roberta-base model, with more parameters and potentially higher performance on downstream tasks. Model inputs and outputs The xlm-roberta-large model takes in text sequences as input and produces contextual embeddings as output. It can be used for a variety of natural language processing tasks, such as text classification, named entity recognition, and question answering. Inputs Text sequences in any of the 100 languages the model was pre-trained on Outputs Contextual word embeddings that capture the meaning and context of the input text The model's logits or probabilities for various downstream tasks, depending on how it is fine-tuned Capabilities The xlm-roberta-large model is a powerful multilingual language model that can be applied to a wide range of NLP tasks across many languages. Its large size and broad language coverage make it suitable for tasks that require understanding text in multiple languages, such as cross-lingual information retrieval or multilingual named entity recognition. What can I use it for? The xlm-roberta-large model is primarily intended to be fine-tuned on downstream tasks, as the pre-trained model alone is not optimized for any specific application. Some potential use cases include: Cross-lingual text classification**: Fine-tune the model on a labeled dataset in one language, then use it to classify text in other languages. Multilingual question answering**: Fine-tune the model on a QA dataset like XNLI to answer questions in multiple languages. Multilingual named entity recognition**: Fine-tune the model on an NER dataset covering multiple languages. See the model hub to look for fine-tuned versions of the xlm-roberta-large model on tasks that interest you. Things to try One interesting aspect of the xlm-roberta-large model is its ability to handle a wide range of languages. You can experiment with feeding the model text in different languages and observe how it performs on tasks like masked language modeling or text generation. Additionally, you can try fine-tuning the model on a multilingual dataset and evaluate its performance on cross-lingual transfer learning.
Updated Invalid Date
❗
xlm-roberta-base
513
The xlm-roberta-base model is a multilingual version of the RoBERTa transformer model, developed by FacebookAI. It was pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages, building on the innovations of the original RoBERTa model. Like RoBERTa, xlm-roberta-base uses the masked language modeling (MLM) objective, which randomly masks 15% of the words in the input and has the model predict the masked words. This allows the model to learn a robust, bidirectional representation of the sentences. The xlm-roberta-base model can be contrasted with other large multilingual models like BERT-base-multilingual-cased, which was trained on 104 languages but used a simpler pre-training objective. The xlm-roberta-base model aims to provide strong cross-lingual transfer learning capabilities by leveraging a much larger and more diverse training dataset. Model inputs and outputs Inputs Text**: The xlm-roberta-base model takes natural language text as input. Outputs Masked word predictions**: The primary output of the model is a probability distribution over the vocabulary for each masked token in the input. Contextual text representations**: The model can also be used to extract feature representations of the input text, which can be useful for downstream tasks like text classification or sequence labeling. Capabilities The xlm-roberta-base model has been shown to perform well on a variety of cross-lingual tasks, outperforming other multilingual models on benchmarks like XNLI and MLQA. It is particularly well-suited for applications that require understanding text in multiple languages, such as multilingual customer support, cross-lingual search, and translation assistance. What can I use it for? The xlm-roberta-base model can be fine-tuned on a wide range of downstream tasks, from text classification to question answering. Some potential use cases include: Multilingual text classification**: Classify documents, social media posts, or other text into categories like sentiment, topic, or intent, across multiple languages. Cross-lingual search and retrieval**: Retrieve relevant documents in one language based on a query in another language. Multilingual question answering**: Build systems that can answer questions posed in different languages by leveraging the model's cross-lingual understanding. Multilingual conversational AI**: Power chatbots and virtual assistants that can communicate fluently in multiple languages. Things to try One interesting aspect of the xlm-roberta-base model is its ability to handle code-switching - the practice of alternating between multiple languages within a single sentence or paragraph. You could experiment with feeding the model text that mixes languages, and observe how well it is able to understand and process the input. Additionally, you could try fine-tuning the model on specialized datasets in different languages to see how it adapts to specific domains and use cases.
Updated Invalid Date