fixie-ai
/

ultravox-v0_3

@@ -1,104 +1,95 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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 ## Evaluation
@@ -128,72 +119,4 @@ Use the code below to get started with the model.
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 ---
+language:
+- en
+license: mit
 library_name: transformers
+datasets:
+- fnlp/AnyInstruct
+- fixie-ai/boolq-audio
+- fixie-ai/soda-audio
+- speechcolab/gigaspeech
 ---
+# Model Card for Ultravox
+Ultravox is a multimodal Speech LLM built around a pretrained [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) and [Whisper-small](https://huggingface.co/openai/whisper-small) backbone.\
+See https://ultravox.ai for the GitHub repo and more information.
 ## Model Details
 ### Model Description
+Ultravox is a multimodal model that can consume both speech and text as input (e.g., a text system prompt and voice user message).
+The input to the model is given as a text prompt with a special `<|audio|>` pseudo-token, and the model processor will replace this magic token with embeddings derived from the input audio.
+Using the merged embeddings as input, the model will then generate output text as usual.
+In a future revision of Ultravox, we plan to expand the token vocabulary to support generation of semantic and acoustic audio tokens, which can then be fed to a vocoder to produce voice output.
+No preference tuning has been applied to this revision of the model.
+- **Developed by:** Fixie.ai
+- **License:** MIT
+### Model Sources
+- **Repository:** https://ultravox.ai
+- **Demo:** See repo
+## Usage
+Think of the model as an LLM that can also hear and understand speech. As such, it can be used as a voice agent,  and also to do speech-to-speech translation, analysis of spoken audio, etc.
+To use the model, try the following:
+```python
+# pip install transformers peft librosa
+import transformers
+import numpy as np
+import librosa
+pipe = transformers.pipeline(model='fixie-ai/ultravox-v0_3', trust_remote_code=True)
+path = "<path-to-input-audio>"  # TODO: pass the audio here
+audio, sr = librosa.load(path, sr=16000)
+turns = [
+  {
+    "role": "system",
+    "content": "You are a friendly and helpful character. You love to answer questions for people."
+  },
+]
+pipe({'audio': audio, 'turns': turns, 'sampling_rate': sr}, max_new_tokens=30)
+```
+## Training Details
+The model uses a pre-trained [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) backbone as well as the encoder part of [Whisper-small](https://huggingface.co/openai/whisper-small).
+The multi-modal adapter is first trained (while keeping backbones frozen) in stage 1 and then in stage 2. Llama3.1 is kept frozen.
 ### Training Data
+Training dataset is a mix of ASR datasets (Gigaspeech), instruction-following and QA data (AnyInstruct and an extended version of BoolQ), and conversational data (SODA with alternative generations for last two turns).
 ### Training Procedure
+Supervised speech to audio finetuning. For more info, see [training code in Ultravox repo](https://github.com/fixie-ai/ultravox/blob/main/ultravox/training/train.py).
 #### Training Hyperparameters
+- **Training regime:** BF16 mixed precision training
+- **Hardward used:** 8x A100-40GB GPUs
+- **LLM LoRA Rank:** 64
+#### Speeds, Sizes, Times
+The current version of Ultravox, when invoked with audio content, has a time-to-first-token (TTFT) of approximately 200ms, and a tokens-per-second rate of ~50-100 when using an A100-40GB GPU, all using a Llama 3.1 8B backbone.
+Check out the audio tab on [TheFastest.ai](https://thefastest.ai/?m=audio) for daily benchmarks and a comparison with other existing models.
 ## Evaluation
 [More Information Needed]
+#### Summary