README.md

metadata

language: fi
datasets:
  - common_voice
metrics:
  - wer
tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
license: apache-2.0
model-index:
  - name: XLSR Wav2Vec2 Finnish by Jonatas Grosman
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice fi
          type: common_voice
          args: fi
        metrics:
          - name: Test WER
            type: wer
            value: 62.39

Wav2Vec2-Large-XLSR-53-Finnish

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Finnish using the Common Voice. When using this model, make sure that your speech input is sampled at 16kHz.

Usage

The model can be used directly (without a language model) as follows:

import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "fi"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-finnish"

test_dataset = load_dataset("common_voice", LANG_ID, split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])

Evaluation

The model can be evaluated as follows on the finnish test data of Common Voice.

import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import homoglyphs as hg

LANG_ID = "fi"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-finnish"
DEVICE = "cuda"

CHARS_TO_IGNORE = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "·", "჻", "¿", "¡", "~", "՞", "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》"]
CURRENCY_SYMBOLS = ["$", "£", "€", "¥", "₩", "₹", "₽", "₱", "₦", "₼", "ლ", "₭", "₴", "₲", "₫", "₡", "₵", "₿", "฿", "¢"]

test_dataset = load_dataset("common_voice", LANG_ID, split="test")
wer = load_metric("wer")

unk_regex = None
if LANG_ID in hg.Languages.get_all():
    # creating regex to match language specific non valid characters
    alphabet = list(hg.Languages.get_alphabet([LANG_ID]))
    valid_chars = alphabet + CURRENCY_SYMBOLS
    unk_regex = "[^"+re.escape("".join(valid_chars))+"\s\d]"

chars_to_ignore_regex = f'[{re.escape("".join(CHARS_TO_IGNORE))}]'

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
model.to(DEVICE)

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    if unk_regex is not None:
        batch["sentence"] = re.sub(unk_regex, "[UNK]", batch["sentence"])
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))

Test Result: 62.39%