Wav2vec2 Ljspeech Gruut

🚀 Wav2Vec2 LJSpeech Gruut

Wav2Vec2 LJSpeech Gruut is an automatic speech recognition model. It's based on the wav2vec 2.0 architecture and fine - tuned on the LJSpech Phonemes dataset. Instead of predicting word sequences, it predicts phoneme sequences, offering unique capabilities in speech recognition.

✨ Features

• Trained to predict sequences of phonemes, with a vocabulary containing different IPA phonemes from gruut.
• Utilizes HuggingFace's PyTorch framework for training.
• Training was conducted on a Google Cloud Engine VM with a Tesla A100 GPU.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoProcessor, AutoModelForCTC, Wav2Vec2Processor
import librosa
import torch
from itertools import groupby
from datasets import load_dataset

def decode_phonemes(
    ids: torch.Tensor, processor: Wav2Vec2Processor, ignore_stress: bool = False
) -> str:
    """CTC-like decoding. First removes consecutive duplicates, then removes special tokens."""
    # removes consecutive duplicates
    ids = [id_ for id_, _ in groupby(ids)]

    special_token_ids = processor.tokenizer.all_special_ids + [
        processor.tokenizer.word_delimiter_token_id
    ]
    # converts id to token, skipping special tokens
    phonemes = [processor.decode(id_) for id_ in ids if id_ not in special_token_ids]

    # joins phonemes
    prediction = " ".join(phonemes)

    # whether to ignore IPA stress marks
    if ignore_stress == True:
        prediction = prediction.replace("ˈ", "").replace("ˌ", "")

    return prediction

checkpoint = "bookbot/wav2vec2-ljspeech-gruut"

model = AutoModelForCTC.from_pretrained(checkpoint)
processor = AutoProcessor.from_pretrained(checkpoint)
sr = processor.feature_extractor.sampling_rate

# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
audio_array = ds[0]["audio"]["array"]

# or, read a single audio file
# audio_array, _ = librosa.load("myaudio.wav", sr=sr)

inputs = processor(audio_array, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs["input_values"]).logits

predicted_ids = torch.argmax(logits, dim=-1)
prediction = decode_phonemes(predicted_ids[0], processor, ignore_stress=True)
# => should give 'b ɪ k ʌ z j u ɚ z s l i p ɪ ŋ ɪ n s t ɛ d ə v k ɔ ŋ k ɚ ɪ ŋ ð ə l ʌ v l i ɹ z p ɹ ɪ n s ə s h æ z b ɪ k ʌ m ə v f ɪ t ə l w ɪ θ n b oʊ p ɹ ə ʃ æ ɡ i s ɪ t s ð ɛ ɹ ə k u ɪ ŋ d ʌ v'

Advanced Usage

There is no advanced usage example in the original document, so this part is not provided.

📚 Documentation

Model Information

Evaluation Results

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 30.0
• mixed_precision_training: Native AMP

Training results

🔧 Technical Details

The model was trained using HuggingFace's PyTorch framework on a Google Cloud Engine VM with a Tesla A100 GPU. All necessary training scripts can be found in the Files and versions tab, and Training metrics were logged via Tensorboard.

📄 License

This model is licensed under the Apache - 2.0 license.

⚠️ Important Note

Do consider the biases which came from pre - training datasets that may be carried over into the results of this model.

👥 Authors

Wav2Vec2 LJSpeech Gruut was trained and evaluated by Wilson Wongso. All computation and development are done on Google Cloud.

🛠 Framework versions

• Transformers 4.26.0.dev0
• Pytorch 1.10.0
• Datasets 2.7.1
• Tokenizers 0.13.2
• Gruut 2.3.4