license: apache-2.0
datasets:
- mozilla-foundation/common_voice_10_0
base_model:
- facebook/wav2vec2-xls-r-300m
tags:
- pytorch
- phoneme-recognition
pipeline_tag: automatic-speech-recognition
metrics:
- per
- aer
library_name: allophant
language:
- bn
- ca
- cs
- cv
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- hi
- hu
- id
- it
- ka
- ky
- lt
- mt
- nl
- pl
- pt
- ro
- ru
- sk
- sl
- sv
- sw
- ta
- tr
- uk
Model Information
Allophant is a multilingual phoneme recognizer trained on spoken sentences in 34 languages, capable of generalizing zero-shot to unseen phoneme inventories.
The model is based on facebook/wav2vec2-xls-r-300m and was pre-trained on a subset of the Common Voice Corpus 10.0 transcribed with eSpeak NG.
| Model Name | UCLA Phonetic Corpus (PER) | UCLA Phonetic Corpus (AER) | Common Voice (PER) | Common Voice (AER) |
|---|---|---|---|---|
| Multitask | 45.62% | 19.44% | 34.34% | 8.36% |
| Hierarchical | 46.09% | 19.18% | 34.35% | 8.56% |
| Multitask Shared | 46.05% | 19.52% | 41.20% | 8.88% |
| Baseline Shared | 48.25% | - | 45.35% | - |
| Baseline | 57.01% | - | 46.95% | - |
Note that our baseline models were trained without phonetic feature classifiers and therefore only support phoneme recognition.
Usage
Install the allophant package:
pip install allophant
A pre-trained model can be loaded from a huggingface checkpoint or local file:
from allophant.estimator import Estimator
device = "cpu"
model, attribute_indexer = Estimator.restore("kgnlp/allophant-baseline", device=device)
supported_features = attribute_indexer.feature_names
# The phonetic feature categories supported by the model, including "phonemes"
print(supported_features)
Allophant supports decoding custom phoneme inventories, which can be constructed in multiple ways:
# 1. For a single language:
inventory = attribute_indexer.phoneme_inventory("es")
# 2. For multiple languages, e.g. in code-switching scenarios
inventory = attribute_indexer.phoneme_inventory(["es", "it"])
# 3. Any custom selection of phones for which features are available in the Allophoible database
inventory = ['a', 'ai̯', 'au̯', 'b', 'e', 'eu̯', 'f', 'ɡ', 'l', 'ʎ', 'm', 'ɲ', 'o', 'p', 'ɾ', 's', 't̠ʃ']
Audio files can then be loaded, resampled and transcribed using the given inventory by first computing the log probabilities for each classifier:
import torch
import torchaudio
from allophant.dataset_processing import Batch
# Load an audio file and resample the first channel to the sample rate used by the model
audio, sample_rate = torchaudio.load("utterance.wav")
audio = torchaudio.functional.resample(audio[:1], sample_rate, model.sample_rate)
# Construct a batch of 0-padded single channel audio, lengths and language IDs
# Language ID can be 0 for inference
batch = Batch(audio, torch.tensor([audio.shape[1]]), torch.zeros(1))
model_outputs = model.predict(
batch.to(device),
attribute_indexer.composition_feature_matrix(inventory).to(device)
)
Finally, the log probabilities can be decoded into the recognized phonemes or phonetic features:
from allophant import predictions
# Create a feature mapping for your inventory and CTC decoders for the desired feature set
inventory_indexer = attribute_indexer.attributes.subset(inventory)
ctc_decoders = predictions.feature_decoders(inventory_indexer, feature_names=supported_features)
for feature_name, decoder in ctc_decoders.items():
decoded = decoder(model_outputs.outputs[feature_name].transpose(1, 0), model_outputs.lengths)
# Print the feature name and values for each utterance in the batch
for [hypothesis] in decoded:
# NOTE: token indices are offset by one due to the <BLANK> token used during decoding
recognized = inventory_indexer.feature_values(feature_name, hypothesis.tokens - 1)
print(feature_name, recognized)
Citation
@inproceedings{glocker2023allophant,
title={Allophant: Cross-lingual Phoneme Recognition with Articulatory Attributes},
author={Glocker, Kevin and Herygers, Aaricia and Georges, Munir},
year={2023},
booktitle={{Proc. Interspeech 2023}},
month={8}}