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library_name: transformers
model_index:
- name: Lance ASR
results: []
tags:
- automatic-speech-recognition
- asr
- pytorch
- transformer
license: apache-2.0
---
# Lance ASR – The Foundation of Speech Intelligence
🚀 **Lance ASR** is a custom-built Automatic Speech Recognition (ASR) model designed for high-efficiency local and cloud inference. It utilizes a Transformer Encoder-Decoder architecture with convolutional subsampling for processing acoustic features.
## 🌟 Key Features
✅ **Custom Architecture**: Not a Whisper clone; features a bespoke Conv1d-subsampling audio front-end.
✅ **Hugging Face Compatible**: Fully integrates with `transformers` via `AutoModelForSeq2SeqLM`.
✅ **Optimized for Precision**: Uses `bfloat16` for high-performance inference and training.
✅ **Scalable Design**: Optimized for 768 hidden dims and 4 layers, balancing speed and accuracy.
✅ **Seamless Tokenization**: Uses the `DWDMaiMai/tiktoken_cl100k_base` tokenizer for efficient text representation.
---
## 📥 Installation & Setup
Load Lance ASR directly from your local directory or the Hugging Face Hub:
```python
import torch
from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForSeq2SeqLM
model_name = "NeuraCraft/Lance-ASR"
tokenizer = AutoTokenizer.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
```
---
## 🛠 Usage Example
Lance ASR can transcribe audio by processing log-mel spectrograms:
```python
# 1. Prepare audio features (e.g., from a .wav file)
# inputs = feature_extractor(audio_array, sampling_rate=16000, return_tensors="pt")
# 2. Generate transcription
model.eval()
with torch.no_grad():
generated_ids = model.generate(
inputs.input_features.to(torch.bfloat16),
max_new_tokens=250,
pad_token_id=tokenizer.eos_token_id
)
transcription = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(f"Transcription: {transcription}")
```
---
📊 Performance & Evaluation
Lance ASR is currently in its early stages, and performance is being actively tested. Initial evaluations focus on:
🔹 **WER (Word Error Rate)** – Measures transcription accuracy
🔹 **CER (Character Error Rate)** – Measures character-level precision
🔹 **Inference Latency** – Optimized for real-time local processing
✅ Planned Enhancements
🔹 Larger training datasets (e.g., Common Voice, LibriSpeech)
🔹 Advanced noise-robustness for real-world environments
🔹 Multilingual ASR support for global accessibility
---
🚀 Future Roadmap
Lance ASR is just getting started! The goal is to transform it into the core auditory component of an advanced AI assistant.
📅 Planned Features:
🔜 Real-time live transcription & streaming support
🔜 Multi-speaker identification (Diarization)
🔜 Integrated Voice Activity Detection (VAD)
🔜 High-efficiency deployment for mobile and edge devices
---
🏗 Development & Contributions
Lance ASR is being developed by **NeuraCraft**. Contributions, suggestions, and testing feedback are welcome!
📬 Contact & Updates:
Developer: NeuraCraft
Project Status: 🚧 In Development
Follow for updates: Coming soon |