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Tabahi/
CUPE-2i

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🗣️ CUPE: Contextless Universal Phoneme Encoder

License: GPLv3

🚀 A PyTorch model for contextless phoneme prediction from speech audio

CUPE processes 120ms frames independently, ensuring each frame's embeddings are acoustically pure—unlike transformer models that mix context across frames.

🔗 Quick Links

🎯 Bournemouth Forced Aligner - For phoneme/word timestamp alignment
📁 CUPE GitHub - Source code repository
🤗 CUPE Hugging Face - Pre-trained models

🎯 Trained Models

📊 Three 30.1M parameter models available

All models are available in the checkpoints directory.

📈 Model Performance

🏷️ Model	🌍 Languages	📊 PER	📊 GER	📝 Description
🇬🇧 English	English	0.24	0.21	🏆 Best quality for English speech
🌍 Multilingual MLS	8 European	0.31	0.26	🇪🇺 en, de, fr, es, pt, it, pl, nl
🌐 Multilingual MSWC	38 languages	0.49	0.39	🗺️ Broad language coverage

📋 Detailed Metrics

🇬🇧 English (New: Oct2025) (en_libri1000_ua01c):

🎯 PER: 0.24 (Phoneme Error Rate)
🎯 GER: 0.22 (Phoneme Group Error Rate)
Fixed rhotics and compound phonemes

🇬🇧 English (en_libri1000_uj01d):

🎯 PER: 0.25 (Phoneme Error Rate)
🎯 GER: 0.23 (Phoneme Group Error Rate)

🌍 Multilingual MLS (multi_MLS8_uh02):

🎯 PER: 0.31
🎯 GER: 0.26

🌐 Multilingual MSWC (multi_mswc38_ug20):

🎯 PER: 0.49
🎯 GER: 0.39

⚠️ Note: CUPE models are designed for contextless phoneme prediction and are not optimal for phoneme classification tasks that require contextual information. CUPE excels at extracting pure, frame-level embeddings that represent the acoustic properties of each phoneme independently of surrounding context.

📚 Datasets

🎵 Training Data Sources

📖 LibriSpeech ASR corpus (SR12): 960 hours of English speech
🌍 Multilingual LibriSpeech (MLS): 800 hours across 8 languages
🗣️ MSWC Multilingual Spoken Words: 240 hours from 50 languages

🔍 Dataset Details

📖 LibriSpeech ASR corpus (SR12):

⏱️ 960 hours of English speech
📁 train-100, train-360, and train-500 splits

🌍 Multilingual LibriSpeech (MLS) (SLR94):

⏱️ 800 hours total (100 hours each)
🌐 8 languages: pl, pt, it, es, fr, nl, de, en

🗣️ MSWC Multilingual Spoken Words Corpus:

⏱️ 240 hours from 50 languages (max 10 hours/language)
🎓 Training: 38 languages (en, de, fr, ca, es, fa, it, ru, pl, eu, cy, eo, nl, pt, tt, cs, tr, et, ky, id, sv-SE, ar, el, ro, lv, sl, zh-CN, ga-IE, ta, vi, gn, or)
🧪 Testing: 6 languages (lt, mt, ia, sk, ka, as)

💡 Need a new language? Start a new discussion and we'll train it for you!

🚀 Installation

⚡ Quick Start (Bournemouth Forced Aligner)

bash

# 📦 Install the package
pip install bournemouth-forced-aligner

# 🔧 Install dependencies
apt-get install espeak-ng ffmpeg

# ❓ Show help
balign --help

📖 See complete BFA guide.

🛠️ Quick Start (CUPE)

bash

# 📦 Install core dependencies
pip install torch torchaudio huggingface_hub

💻 Easy Usage with Automatic Download

🎯 Zero-setup required - automatic downloads from Hugging Face Hub

🦋 Example Output

Running with sample audio 🦋 butterfly.wav:

bash

🔄 Loading CUPE english model...
✅ Model loaded on cpu
🎵 Processing audio: 1.26s duration
📊 Processed 75 frames (1200ms total)

📋 Results:
🔤 Phoneme predictions shape: (75,)
🏷️ Group predictions shape: (75,)
ℹ️ Model info: {'model_name': 'english', 'sample_rate': 16000, 'frames_per_second': 62.5}

🔍 First 10 frame predictions:
Frame 0: phoneme=66, group=16
Frame 1: phoneme=66, group=16
Frame 2: phoneme=29, group=7
...

🔤 Phonemes: ['b', 'ʌ', 't', 'h', 'ʌ', 'f', 'l', 'æ']...
🏷️ Groups: ['voiced_stops', 'central_vowels', 'voiceless_stops']...

🐍 Python Code

python

import torch
import torchaudio
from huggingface_hub import hf_hub_download
import importlib.util

def load_cupe_model(model_name="english", device="auto"):
    """🔄 Load CUPE model with automatic downloading from Hugging Face Hub"""
    
    model_files = {
        "english": "en_libri1000_uj01d_e199_val_GER=0.2307.ckpt",
        "multilingual-mls": "multi_MLS8_uh02_e36_val_GER=0.2334.ckpt", 
        "multilingual-mswc": "multi_mswc38_ug20_e59_val_GER=0.5611.ckpt"
    }
    
    if device == "auto":
        device = "cuda" if torch.cuda.is_available() else "cpu"
    
    # 📥 Download files automatically from Hugging Face Hub
    repo_id = "Tabahi/CUPE-2i"
    model_file = hf_hub_download(repo_id=repo_id, filename="model2i.py")
    windowing_file = hf_hub_download(repo_id=repo_id, filename="windowing.py") 
    checkpoint = hf_hub_download(repo_id=repo_id, filename=f"ckpt/{model_files[model_name]}")
    model_utils_file = hf_hub_download(repo_id=repo_id, filename="model_utils.py")
    
    # 🔧 Import modules dynamically
    _ = import_module_from_file("model_utils", model_utils_file)
    spec = importlib.util.spec_from_file_location("model2i", model_file)
    model2i = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(model2i)
    
    spec = importlib.util.spec_from_file_location("windowing", windowing_file)
    windowing = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(windowing)
    
    # 🚀 Initialize model
    extractor = model2i.CUPEEmbeddingsExtractor(checkpoint, device=device)
    return extractor, windowing

# 🎯 Example usage
extractor, windowing = load_cupe_model("english")

# 🎵 Load and process your audio
audio, sr = torchaudio.load("your_audio.wav")
if sr != 16000:
    resampler = torchaudio.transforms.Resample(sr, 16000)
    audio = resampler(audio)

# 📊 Add batch dimension and process
audio_batch = audio.unsqueeze(0)
windowed_audio = windowing.slice_windows(audio_batch, 16000, 120, 80)
batch_size, num_windows, window_size = windowed_audio.shape
windows_flat = windowed_audio.reshape(-1, window_size)

# 🔮 Get predictions
logits_phonemes, logits_groups = extractor.predict(windows_flat, return_embeddings=False, groups_only=False)

print(f"🔤 Phoneme logits shape: {logits_phonemes.shape}")  # [num_windows, frames_per_window, 66]
print(f"🏷️ Group logits shape: {logits_groups.shape}")     # [num_windows, frames_per_window, 16]

🔧 Advanced Usage (Manual Setup)

📁 Manual Setup Code

For more control, see run.py:

python

import torch
import torchaudio
from model2i import CUPEEmbeddingsExtractor  # 🎯 Main CUPE model feature extractor
import windowing  # 🔧 Provides slice_windows, stich_window_predictions

# 📁 Load model from local checkpoint
cupe_ckpt_path = "./ckpt/en_libri1000_uj01d_e199_val_GER=0.2307.ckpt"
extractor = CUPEEmbeddingsExtractor(cupe_ckpt_path, device="cuda")

# 🎵 Prepare audio
sample_rate = 16000
window_size_ms = 120
stride_ms = 80
max_wav_len = 10 * sample_rate  # 10 seconds

dummy_wav = torch.zeros(1, max_wav_len, dtype=torch.float32, device="cpu")
audio_batch = dummy_wav.unsqueeze(0)  # Add batch dimension

# 🪟 Window the audio
windowed_audio = windowing.slice_windows(
    audio_batch.to("cuda"),
    sample_rate,
    window_size_ms,
    stride_ms
)
batch_size, num_windows, window_size = windowed_audio.shape
windows_flat = windowed_audio.reshape(-1, window_size)

# 🔮 Get predictions
logits, _ = extractor.predict(windows_flat, return_embeddings=False, groups_only=False)

# 🔄 Reshape and stitch window predictions
frames_per_window = logits.shape[1]
logits = logits.reshape(batch_size, num_windows, frames_per_window, -1)
logits = windowing.stich_window_predictions(
    logits,
    original_audio_length=audio_batch.size(2),
    cnn_output_size=frames_per_window,
    sample_rate=sample_rate,
    window_size_ms=window_size_ms,
    stride_ms=stride_ms
)

print(f"📊 Output shape: {logits.shape}")  # [B, T, 66]

📊 Output Format

🔤 Phoneme logits: (time_frames, 66) - 66 IPA phoneme classes
🏷️ Group logits: (time_frames, 16) - 16 phoneme groups
⏱️ Time resolution: ~16ms per frame (~62.5 FPS)
🗺️ Mapping: See mapper.py for phoneme-to-index mapping

✨ Key Features

🚀 No manual downloads - automatic via Hugging Face Hub
🌍 Multiple languages - English + 37 other languages
⚡ Real-time capable - faster than real-time on GPU
⏱️ Frame-level timing - 16ms resolution
🎯 Contextless - each frame processed independently

🎨 Custom Dataset for Training

🔧 Training Setup

📋 See mapper.py for tokenization (66 phonemes + 16 groups)
🔤 Use IPA-based grapheme-to-phoneme tools: Espeak-ng
📝 Convert words to IPA sequences: phonemizer
🗺️ Map IPA phonemes to tokens: IPAPhonemeMapper

Token Mapping:

Token 0: 🔇 Silence
Tokens 1-65: 🔤 IPA phonemes
Token 66: 📻 Blank/noise

🎯 Use Cases

⏰ Timestamp alignment (examples coming soon)
📊 Speech analysis
🔍 Phoneme recognition
🎵 Audio processing

📊 Visual Results

📈 Sample Probabilities Timeline

🌍 Multilingual Confusion Plot

🇬🇧 English-only Confusion Plot

📖 Citation

📄 Paper: CUPE: Contextless Universal Phoneme Encoder for Language-Agnostic Speech Processing

bibtex

@inproceedings{rehman2025cupe,
  title     = {CUPE: Contextless Universal Phoneme Encoder for Language-Agnostic Speech Processing},
  author    = {Abdul Rehman and Jian-Jun Zhang and Xiaosong Yang},
  booktitle = {Proceedings of the 8th International Conference on Natural Language and Speech Processing (ICNLSP 2025)},
  year      = {2025},
  organization = {ICNLSP},
  publisher = {International Conference on Natural Language and Speech Processing},
}

Tabahi/CUPE-2i

🗣️ CUPE: Contextless Universal Phoneme Encoder

🔗 Quick Links

🎯 Trained Models

📈 Model Performance

📚 Datasets

🎵 Training Data Sources

🚀 Installation

⚡ Quick Start (Bournemouth Forced Aligner)

🛠️ Quick Start (CUPE)

💻 Easy Usage with Automatic Download

🦋 Example Output

🐍 Python Code

🔧 Advanced Usage (Manual Setup)

📊 Output Format

✨ Key Features

🎨 Custom Dataset for Training

🎯 Use Cases

📊 Visual Results

📈 Sample Probabilities Timeline

🌍 Multilingual Confusion Plot

🇬🇧 English-only Confusion Plot

📖 Citation

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Tabahi/
CUPE-2i