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---
language: en
license: apache-2.0
tags:
- phoneme-recognition
- generated_from_trainer
datasets:
- w11wo/ljspeech_phonemes
base_model: Wav2Vec2-Base
model-index:
- name: Wav2Vec2 LJSpeech Gruut
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: LJSpeech
type: ljspeech_phonemes
metrics:
- type: per
value: 0.0099
name: Test PER (w/o stress)
- type: cer
value: 0.0058
name: Test CER (w/o stress)
---
# Wav2Vec2 LJSpeech Gruut
Wav2Vec2 LJSpeech Gruut is an automatic speech recognition model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a fine-tuned version of [Wav2Vec2-Base](https://huggingface.co/facebook/wav2vec2-base) on the [LJSpech Phonemes](https://huggingface.co/datasets/w11wo/ljspeech_phonemes) dataset.
Instead of being trained to predict sequences of words, this model was trained to predict sequence of phonemes, e.g. `["h", "ɛ", "l", "ˈoʊ", "w", "ˈɚ", "l", "d"]`. Therefore, the model's [vocabulary](https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/blob/main/vocab.json) contains the different IPA phonemes found in [gruut](https://github.com/rhasspy/gruut).
This model was trained using HuggingFace's PyTorch framework. All training was done on a Google Cloud Engine VM with a Tesla A100 GPU. All necessary scripts used for training could be found in the [Files and versions](https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/tree/main) tab, as well as the [Training metrics](https://huggingface.co/bookbot/wav2vec2-ljspeech-gruut/tensorboard) logged via Tensorboard.
## Model
| Model | #params | Arch. | Training/Validation data (text) |
| ------------------------- | ------- | ----------- | ------------------------------- |
| `wav2vec2-ljspeech-gruut` | 94M | wav2vec 2.0 | `LJSpech Phonemes` Dataset |
## Evaluation Results
The model achieves the following results on evaluation:
| Dataset | PER (w/o stress) | CER (w/o stress) |
| ---------------------------- | :--------------: | :--------------: |
| `LJSpech Phonemes` Test Data | 0.99% | 0.58% |
## Usage
```py
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'
```
## 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
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
| :-----------: | :---: | :---: | :-------------: | :----: | :----: |
| No log | 1.0 | 348 | 2.2818 | 1.0 | 1.0 |
| 2.6692 | 2.0 | 696 | 0.2045 | 0.0527 | 0.0299 |
| 0.2225 | 3.0 | 1044 | 0.1162 | 0.0319 | 0.0189 |
| 0.2225 | 4.0 | 1392 | 0.0927 | 0.0235 | 0.0147 |
| 0.0868 | 5.0 | 1740 | 0.0797 | 0.0218 | 0.0143 |
| 0.0598 | 6.0 | 2088 | 0.0715 | 0.0197 | 0.0128 |
| 0.0598 | 7.0 | 2436 | 0.0652 | 0.0160 | 0.0103 |
| 0.0447 | 8.0 | 2784 | 0.0571 | 0.0152 | 0.0095 |
| 0.0368 | 9.0 | 3132 | 0.0608 | 0.0163 | 0.0112 |
| 0.0368 | 10.0 | 3480 | 0.0586 | 0.0137 | 0.0083 |
| 0.0303 | 11.0 | 3828 | 0.0641 | 0.0141 | 0.0085 |
| 0.0273 | 12.0 | 4176 | 0.0656 | 0.0131 | 0.0079 |
| 0.0232 | 13.0 | 4524 | 0.0690 | 0.0133 | 0.0082 |
| 0.0232 | 14.0 | 4872 | 0.0598 | 0.0128 | 0.0079 |
| 0.0189 | 15.0 | 5220 | 0.0671 | 0.0121 | 0.0074 |
| 0.017 | 16.0 | 5568 | 0.0654 | 0.0114 | 0.0069 |
| 0.017 | 17.0 | 5916 | 0.0751 | 0.0118 | 0.0073 |
| 0.0146 | 18.0 | 6264 | 0.0653 | 0.0112 | 0.0068 |
| 0.0127 | 19.0 | 6612 | 0.0682 | 0.0112 | 0.0069 |
| 0.0127 | 20.0 | 6960 | 0.0678 | 0.0114 | 0.0068 |
| 0.0114 | 21.0 | 7308 | 0.0656 | 0.0111 | 0.0066 |
| 0.0101 | 22.0 | 7656 | 0.0669 | 0.0109 | 0.0066 |
| 0.0092 | 23.0 | 8004 | 0.0677 | 0.0108 | 0.0065 |
| 0.0092 | 24.0 | 8352 | 0.0653 | 0.0104 | 0.0063 |
| 0.0088 | 25.0 | 8700 | 0.0673 | 0.0102 | 0.0063 |
| 0.0074 | 26.0 | 9048 | 0.0669 | 0.0105 | 0.0064 |
| 0.0074 | 27.0 | 9396 | 0.0707 | 0.0101 | 0.0061 |
| 0.0066 | 28.0 | 9744 | 0.0673 | 0.0100 | 0.0060 |
| 0.0058 | 29.0 | 10092 | 0.0689 | 0.0100 | 0.0059 |
| 0.0058 | 30.0 | 10440 | 0.0683 | 0.0099 | 0.0058 |
## Disclaimer
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](https://w11wo.github.io/). 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 |