| | --- |
| | 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 |
