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Ultravox V0 4 1 Mistral Nemo
Overview :
ultravox-v0_4_1-mistral-nemo is a multimodal speech large language model (LLM) based on pre-trained Mistral-Nemo-Instruct-2407 and whisper-large-v3-turbo. The model can handle both speech and text input simultaneously, such as a text system prompt and a speech user message. Ultravox converts input audio into embeddings using a special <|audio|> pseudo-token and generates output text. Future versions plan to expand the token vocabulary to support generating semantic and acoustic audio tokens, which can then be input into a vocoder to produce speech output. The model is developed by Fixie.ai and is licensed under MIT.
Target Users :
Ultravox targets developers and businesses that need to process speech and text data, such as professionals in speech recognition, speech translation, and speech analysis. Its multimodal processing capabilities and efficient training methods make it particularly suitable for users who need to quickly and accurately process and generate speech and text information.
Use Cases
- Act as a voice agent, handling users' voice commands.
- Perform speech-to-speech translation to facilitate cross-language communication.
- Analyze speech audio to extract key information for security monitoring or customer service.
Features
- Speech and Text Input Processing: Able to handle both speech and text input simultaneously, suitable for various applications.
- Audio Embedding Replacement: Uses the <|audio|> pseudo-token to convert input audio into embeddings, improving the model's multimodal processing capabilities.
- Speech-to-Speech Translation: Suitable for speech translation, speech audio analysis, and other scenarios.
- Model Text Generation: Generates output text based on merged embedding input.
- Future Support for Semantic and Acoustic Audio Tokens: Plans to support generating semantic and acoustic audio tokens in future versions, further expanding model functionality.
- Knowledge Distillation Loss Training: Trained using knowledge distillation loss, making the Ultravox model attempt to match the logits of the text-based Mistral backbone.
- Mixed Precision Training: Uses BF16 mixed precision training to improve training efficiency.
How to Use
1. Install necessary libraries: Install the transformers, peft, and librosa libraries using pip.
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2. Import libraries: Import the transformers, numpy, and librosa libraries into your code.
3. Load the model: Load the 'fixie-ai/ultravox-v0_4_1-mistral-nemo' model using transformers.pipeline.
4. Prepare audio input: Load the audio file using librosa.load and obtain the audio data and sample rate.
5. Define conversation turns: Create a list of conversation turns containing the system role and content.
6. Call the model: Call the model to generate output text, passing the audio data, conversation turns, and sample rate as parameters.
7. Get the results: The model will output the generated text, which can be used for further processing or display.
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