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Spirit LM
Overview :
Spirit LM is a fundamental multimodal language model that can freely combine text and speech. The model is based on a 7B pretrained text language model and extends to the speech modality through continuous training on both text and speech units. Speech and text sequences are concatenated into a single token stream and trained using a small automatically curated speech-text parallel corpus with a word-level interleaving approach. Spirit LM offers two versions: the basic version uses speech phoneme units (HuBERT), while the expressive version adds pitch and style units to simulate expressiveness. For both versions, text is encoded using subword BPE tokens. This model not only demonstrates the semantic capabilities of text models but also showcases the expressive abilities of speech models. Furthermore, we demonstrate that Spirit LM can learn new tasks across modalities with few samples (e.g., ASR, TTS, speech classification).
Target Users :
Spirit LM is designed for researchers and developers in the field of Natural Language Processing (NLP), particularly those interested in multimodal language models. This product is well-suited for them as it provides a powerful tool to handle and understand data that combines text and speech, which is critical for developing more natural and intuitive human-computer interaction systems. Additionally, it aids researchers in quickly training and deploying new task models with minimal samples, thereby accelerating the research and development process.
Use Cases
Example 1: Use the Spirit LM basic version for automatic speech recognition (ASR) on a speech input and generate the corresponding text output.
Example 2: Utilize the Spirit LM expressive version to analyze the emotion and style of a speech segment, replicating the same emotional expression in text generation.
Example 3: In the education sector, use Spirit LM to develop an application that assists in language learning, able to understand and respond to students' speech input while providing text feedback.
Features
? Multimodal processing: The model can handle data from both text and speech modalities.
? Word-level interleaved training: Trained using a small-scale speech-text parallel corpus, enabling word-level interleaving.
? Two versions: Offers both basic and expressive versions, with the latter incorporating pitch and style units for expressiveness.
? Subword BPE encoding: Text is encoded using subword BPE tokens, enhancing the model's flexibility and accuracy.
? Cross-modal task learning: Capable of learning new tasks with few samples, such as automatic speech recognition (ASR), text-to-speech (TTS), and speech classification.
? Semantic and expressive capabilities: Combines the semantic understanding of text models with the expressive abilities of speech models.
? Automatically curated corpus: Employs an automatically curated speech-text parallel corpus, reducing manual intervention.
How to Use
1. Visit the official GitHub page of Spirit LM or the relevant papers to understand the model's basic information and prerequisites.
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2. Choose between the basic or expressive version of Spirit LM as needed, and download the corresponding pretrained model.
3. Prepare or obtain a speech-text parallel corpus for training and fine-tuning the model.
4. Use the interfaces provided by the model to input text or speech data and specify the desired output modality.
5. Fine-tune the model according to the application scenario to adapt it to specific tasks or datasets.
6. After training and fine-tuning the model, integrate Spirit LM into your application or research project.
7. Evaluate the model's performance to ensure it meets your application requirements.
8. Iteratively optimize the model as needed to improve its performance on specific tasks.
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