metadata

license: agpl-3.0
datasets:
  - KoelLabs/DoReCo
  - KoelLabs/EpaDB
  - KoelLabs/SpeechOceanNoTH
  - KoelLabs/L2Arctic
  - KoelLabs/L2ArcticSpontaneousSplit
  - KoelLabs/Buckeye
  - KoelLabs/TIMIT
language:
  - en
metrics:
  - name: Phonemic Error Rate (PER)
    type: cer
    value: 0.187093
  - name: Feature Error Rate (FER)
    type: cer
    value: 0.034725
base_model:
  - facebook/wav2vec2-xlsr-53-espeak-cv-ft
tags:
  - Speech2IPA

Koel Labs XLSR English 01

The goal of this phoneme transcription model is to robustly model accented and impaired English pronunciation for use in pronunciation feedback systems and linguistic analysis.

We predict sequences of phonemes describing the sounds of speech in an audio clip using a vocabulary of 79 symbols from the International Phonetic Alphabet.

We train on roughly 41 hours of speech from TIMIT, Buckeye, L2-ARCTIC, EpaDB, Speech Ocean, DoReCo, and PSST.

We then evaluate on 15 hours of speech from the ISLE Corpus and the test sets from TIMIT, EpaDB, PSST, and Speech Ocean.

Normalizing scores by native accent and speech impairment, we achieve roughly 19% PER and 3% FER (lower is better):

Scores by Dataset

We significantly improve over our previous model (Koel Labs B0) even on TIMIT which it was finetuned on:

We also perform well on the ISLE Corpus which contains accents not seen during training (German and Italian).

Method

We employ a number of strategies that enables us to generalize well to unseen speakers and dialects. Most notable are the following:

Vocabulary refinement: consolidating notation inconsistencies across dataset conventions and fixing misparsed tokens in pretrained checkpoints.
Curriculum learning: combining XLS-R self-supervised learning, multilingual ASR pretraining, G2P finetuning, and high quality human annotated data.

Usage

First pip install transformers, then run the following python code:

from transformers import pipeline

model = pipeline(
  "automatic-speech-recognition",
  model="KoelLabs/xlsr-english-01",
  device="cpu",
)
print("Transcription:", model("path to your audio file").get("text", ""))

To get timestamps for each phoneme, pip install soundfile librosa and run the following code instead:

import torch
import soundfile as sf
from transformers import AutoProcessor, AutoModelForCTC

device = (
  "cuda"
  if torch.cuda.is_available()
  else "mps" if torch.backends.mps.is_available() else "cpu"
)
model_id = "KoelLabs/xlsr-english-01"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForCTC.from_pretrained(model_id).to(device)

array, sample_rate = sf.read(filename)
array = librosa.resample(array, orig_sr=sample_rate, target_sr=processor.feature_extractor.sampling_rate)
batch = [array]

input_values = (
  processor(
    batch,
    sampling_rate=processor.feature_extractor.sampling_rate,
    return_tensors="pt",
    padding=True,
  )
  .input_values.type(torch.float32)
  .to(model.device)
)
with torch.no_grad():
  logits = model(input_values).logits
predicted_ids_batch = torch.argmax(logits, dim=-1)
transcription_batch = [processor.decode(ids) for ids in predicted_ids_batch]

# get the start and end timestamp for each phoneme
phonemes_with_time_batch = []
for predicted_ids in predicted_ids_batch:
  predicted_ids = predicted_ids.tolist()
  duration_sec = input_values.shape[1] / processor.feature_extractor.sampling_rate

  ids_w_time = [
      (i / len(predicted_ids) * duration_sec, _id)
      for i, _id in enumerate(predicted_ids)
  ]

  current_phoneme_id = processor.tokenizer.pad_token_id
  current_start_time = 0
  phonemes_with_time = []
  for time, _id in ids_w_time:
    if current_phoneme_id != _id:
      if current_phoneme_id != processor.tokenizer.pad_token_id:
        phonemes_with_time.append(
          (processor.decode(current_phoneme_id), current_start_time, time)
        )
      current_start_time = time
      current_phoneme_id = _id

  phonemes_with_time_batch.append(phonemes_with_time)

print(transcription_batch)
print(phonemes_with_time_batch)