fixie-ai
/

ultravox-v0_4-llama-3_1-70b

@@ -1,199 +1,113 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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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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 ---
+language:
+- en
+- ar
+- de
+- es
+- fr
+- it
+- ja
+- pt
+- ru
+license: mit
 library_name: transformers
+datasets:
+- fixie-ai/librispeech_asr
+- fixie-ai/common_voice_17_0
+- fixie-ai/peoples_speech
+- fnlp/AnyInstruct
+metrics:
+- bleu
 ---
+# Model Card for Ultravox
+Ultravox is a multimodal Speech LLM built around a pretrained [Llama3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B) and [Whisper-medium](https://huggingface.co/openai/whisper-medium) 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_4', 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 [Llama3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B) backbone as well as the encoder part of [Whisper-medium](https://huggingface.co/openai/whisper-medium).
+Only the multi-modal adapter is trained, while Whisper encoder and Llama are kept frozen.
+We use a knowledge-distillation loss where Ultravox is trying to match the logits of the text-based Llama backbone.
 ### Training Data
+Training dataset is a mix of ASR datasets, extended by adding a "continuation" generated by Llama 3.1 70B.
 ### 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 H100 GPUs
+#### Speeds, Sizes, Times
+The current version of Ultravox, when invoked with audio content, has a time-to-first-token (TTFT) of approximately 400ms, and a tokens-per-second rate of ~50-100 when using 4xA100-40GB GPU, all using a Llama 3.1 70B 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
+|                   | en_de (BLEU) | es_en (BLEU) | LibriSpeech clean.test (WER) |
+|:------------------|:-------------|:-------------|:----------------------------|
+| Ultravox v0.3 | 22.66        | 24.74        | 6.67                        |
+| Ultravox v0.4 8B | 25.47        | 37.11        | 4.45                        |
+| **Ultravox v0.4 70B** | **30.30**        | **39.55**        | **4.49**                        |
+| Llama3.1 8B (text-only) | 31.95     | 44.62        | -                           |
+| Llama3.1 70B (text-only) | 32.59     | 46.39        | -                           |