README.md

---
library_name: transformers
tags:
- VAD
- audio
- transformer
- endpointing
---

# Model Card: UltraVAD

UltraVAD is a context-aware, audio-native endpointing model. It estimates the probability that a speaker has finished their turn in real time by fusing recent dialog text with the user’s audio. UltraVAD consumes the dialogue history and the last user audio turn, then produces a probability for the end-of-turn token `<|eot_id|>`.

## Model Details

- **Developer:** Ultravox.ai  
- **Type:** Context-aware audio–text fusion endpointing  
- **Backbone:** Llama-8B (post-trained)  
- **Languages (26):** ar, bg, zh, cs, da, nl, en, fi, fr, de, el, hi, hu, it, ja, pl, pt, ro, ru, sk, es, sv, ta, tr, uk, vi

**What it predicts.** UltraVAD computes the probability `P(<eot_id> | context, user_audio)`

## Sources

- **Website/Repo:** https://ultravox.ai  
- **Demo:** https://demo.ultravox.ai/  
- **Benchmark:** https://huggingface.co/datasets/fixie-ai/turntaking-contextual-tts
- **Blog:** https://www.ultravox.ai/blog/ultravad-is-now-open-source-introducing-the-first-context-aware-audio-native-endpointing-model

## Usage

Use UltraVAD as a turn-taking oracle in voice agents. Run it alongside a lightweight streaming VAD; when short silences are detected, call UltraVAD and trigger your agent’s response once the `<eot>` probability crosses your threshold.

```python
import transformers
import torch
import librosa
import os

pipe = transformers.pipeline(model='fixie-ai/ultraVAD', trust_remote_code=True, device="cpu")

sr = 16000
wav_path = os.path.join(os.path.dirname(__file__), "sample.wav")
audio, sr = librosa.load(wav_path, sr=sr)

turns = [
  {"role": "assistant", "content": "Hi, how are you?"},
]

# Build model inputs via pipeline preprocess
inputs = {"audio": audio, "turns": turns, "sampling_rate": sr}
model_inputs = pipe.preprocess(inputs)

# Move tensors to model device
device = next(pipe.model.parameters()).device
model_inputs = {k: (v.to(device) if hasattr(v, "to") else v) for k, v in model_inputs.items()}

# Forward pass (no generation)
with torch.inference_mode():
  output = pipe.model.forward(**model_inputs, return_dict=True)

# Compute last-audio token position
logits = output.logits  # (1, seq_len, vocab)
audio_pos = int(
  model_inputs["audio_token_start_idx"].item() +
  model_inputs["audio_token_len"].item() - 1
)

# Resolve <|eot_id|> token id and compute probability at last-audio index
token_id = pipe.tokenizer.convert_tokens_to_ids("<|eot_id|>")
if token_id is None or token_id == pipe.tokenizer.unk_token_id:
  raise RuntimeError("<|eot_id|> not found in tokenizer.")

audio_logits = logits[0, audio_pos, :]
audio_probs = torch.softmax(audio_logits.float(), dim=-1)
eot_prob_audio = audio_probs[token_id].item()
print(f"P(<|eot_id|>) = {eot_prob_audio:.6f}")
threshold = 0.1
if eot_prob_audio > threshold:
  print("Is End of Turn")
else:
  print("Is Not End of Turn")
```

## Training

Text-only post-training (LLM):
Post-train the backbone to predict <eot> in dialog, yielding a probability over likely stop points rather than brittle binary labels.
Data: Synthetic conversational corpora with inserted <eot> tokens, translation-augmented across 26 languages.

Audio-native fusion (Ultravox projector):
Attach and fine-tune the Ultravox audio projector so the model conditions jointly on audio embeddings and text, aligning prosodic cues with the <eot> objective.
Data: Robust to real-world noise, device/mic variance, overlapping speech.

Calibration:
Choose a decision threshold to balance precision vs. recall per language/domain. Recommended starting threshold: 0.1. 
Raise the threshold if you find the model interrupting too eagerly, and lower the threshold if you find the model not responding when its supposed to.

## Performance & Deployment

Latency (forward pass): ~65-110 ms on an A6000.

Common pattern: Pair with a streaming VAD (e.g., Silero). Invoke UltraVAD on short silences; its latency is often hidden under TTS time-to-first-token.

## Evaluation

UltraVAD is evaluated on both context-dependent and single-turn datasets.

Contextual benchmark: 400 held-out samples requiring dialog history (fixie-ai/turntaking-contextual-tts).

Single-turn sets: Smart-Turn V2’s Orpheus synthetic datasets (aggregate).

Results

Context-dependent turn-taking (400 held-out samples)
| **Metric** | **UltraVAD** | **Smart-Turn V2** |
|---|---:|---:|
| **Accuracy** | 77.5% | 63.0% |
| **Precision** | 69.6% | 59.8% |
| **Recall** | 97.5% | 79.0% |
| **F1-Score** | 81.3% | 68.1% |
| **AUC** | 89.6% | 70.0% |


Single-turn datasets (Orpheus aggregate)
| **Dataset** | **UltraVAD** | **Smart-Turn V2** |
|---|---:|---:|
| **orpheus-aggregate-test** | 93.7% | 94.3% |