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metadata
language: jv
datasets:
  - openslr
metrics:
  - wer
tags:
  - audio
  - automatic-speech-recognition
  - speech
  - xlsr-fine-tuning-week
license: apache-2.0
model-index:
  - name: XLSR Wav2Vec2 Javanese by cahya
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: OpenSLR High quality TTS data for Javanese
          type: OpenSLR
          args: jv
        metrics:
          - name: Test WER
            type: wer
            value: 17.61

Wav2Vec2-Large-XLSR-Javanese

Fine-tuned facebook/wav2vec2-large-xlsr-53 on the OpenSLR High quality TTS data for Javanese. 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, load_metric, Dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets.utils.download_manager import DownloadManager
from pathlib import Path
import pandas as pd

def load_dataset_javanese():
    urls = [
        "https://www.openslr.org/resources/41/jv_id_female.zip",
        "https://www.openslr.org/resources/41/jv_id_male.zip"
    ]
    dm = DownloadManager()
    download_dirs = dm.download_and_extract(urls)
    data_dirs = [ 
        Path(download_dirs[0])/"jv_id_female/wavs",
        Path(download_dirs[1])/"jv_id_male/wavs",
    ]
    filenames = [ 
        Path(download_dirs[0])/"jv_id_female/line_index.tsv",
        Path(download_dirs[1])/"jv_id_male/line_index.tsv",
    ]
    
    dfs = []
    dfs.append(pd.read_csv(filenames[0], sep='\t', names=["path", "sentence"]))
    dfs.append(pd.read_csv(filenames[1], sep='\t', names=["path", "client_id", "sentence"]))
    dfs[1] = dfs[1].drop(["client_id"], axis=1)
    
    for i, dir in enumerate(data_dirs):
        dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
    df = pd.concat(dfs)
    # df = df.sample(frac=1, random_state=1).reset_index(drop=True)
    dataset = Dataset.from_pandas(df)
    dataset = dataset.remove_columns('__index_level_0__')
    
    return dataset.train_test_split(test_size=0.1, seed=1)

dataset = load_dataset_javanese()
test_dataset = dataset['test']

processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")

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 or using this notebook

import torch
import torchaudio
from datasets import load_dataset, load_metric, Dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
from pathlib import Path
import pandas as pd

def load_dataset_javanese():
    urls = [
        "https://www.openslr.org/resources/41/jv_id_female.zip",
        "https://www.openslr.org/resources/41/jv_id_male.zip"
    ]
    dm = DownloadManager()
    download_dirs = dm.download_and_extract(urls)
    data_dirs = [
        Path(download_dirs[0])/"jv_id_female/wavs",
        Path(download_dirs[1])/"jv_id_male/wavs",
    ]
    filenames = [
        Path(download_dirs[0])/"jv_id_female/line_index.tsv",
        Path(download_dirs[1])/"jv_id_male/line_index.tsv",
    ]

    dfs = []
    dfs.append(pd.read_csv(filenames[0], sep='\t', names=["path", "sentence"]))
    dfs.append(pd.read_csv(filenames[1], sep='\t', names=["path", "client_id", "sentence"]))
    dfs[1] = dfs[1].drop(["client_id"], axis=1)

    for i, dir in enumerate(data_dirs):
        dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
    df = pd.concat(dfs)
    # df = df.sample(frac=1, random_state=1).reset_index(drop=True)
    dataset = Dataset.from_pandas(df)
    dataset = dataset.remove_columns('__index_level_0__')

    return dataset.train_test_split(test_size=0.1, seed=1)

dataset = load_dataset_javanese()
test_dataset = dataset['test']

wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-javanese") 
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”_\�]'

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()
    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("cuda"), attention_mask=inputs.attention_mask.to("cuda")).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: 17.61 %

Training

OpenSLR High quality TTS data for Javanese was used for training. The script used for training can be found here and to evaluate it

The Common Voice train, validation, and ... datasets were used for training as well as ... and ... # TODO

The script used for training can be found here