| --- |
| language: |
| - tr |
| datasets: |
| - common_voice |
| - movies |
| metrics: |
| - wer |
| tags: |
| - audio |
| - automatic-speech-recognition |
| - speech |
| - xlsr-fine-tuning-week |
| license: apache-2.0 |
| model-index: |
| - name: XLSR Wav2Vec2 Large Turkish with extended dataset by Gorkem Goknar |
| results: |
| - task: |
| name: Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: Common Voice tr |
| type: common_voice |
| args: tr |
| metrics: |
| - name: Test WER |
| type: wer |
| value: 50.41 |
| --- |
| # Wav2Vec2-Large-XLSR-53-Turkish |
|
|
| Note: This model is trained with 5 Turkish movies additional to common voice dataset. |
| Although WER is high (50%) per common voice test dataset, performance from "other sources " seems pretty good. |
|
|
| Disclaimer: Please use another wav2vec2-tr model in hub for "clean environment" dialogues as they tend to do better in clean sounds with less background noise. |
|
|
| Dataset building from csv and merging code can be found on below of this Readme. |
|
|
| Please try speech yourself on the right side to see its performance. |
|
|
| Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice) and 5 Turkish movies that include background noise/talkers . |
|
|
| 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: |
| ```python |
| import torch |
| import torchaudio |
| import pydub |
| from pydub.utils import mediainfo |
| import array |
| from pydub import AudioSegment |
| from pydub.utils import get_array_type |
| import numpy as np |
| |
| from datasets import load_dataset |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
| test_dataset = load_dataset("common_voice", "tr", split="test[:2%]") |
| processor = Wav2Vec2Processor.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish") |
| model = Wav2Vec2ForCTC.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish") |
| |
| |
| |
| new_sample_rate = 16000 |
| |
| def audio_resampler(batch, new_sample_rate = 16000): |
| |
| #not working without complex library compilation in windows for mp3 |
| #speech_array, sampling_rate = torchaudio.load(batch["path"]) |
| #speech_array, sampling_rate = librosa.load(batch["path"]) |
| |
| #sampling_rate = pydub.utils.info['sample_rate'] ##gets current samplerate |
| |
| sound = pydub.AudioSegment.from_file(file=batch["path"]) |
| sampling_rate = new_sample_rate |
| sound = sound.set_frame_rate(new_sample_rate) |
| left = sound.split_to_mono()[0] |
| bit_depth = left.sample_width * 8 |
| array_type = pydub.utils.get_array_type(bit_depth) |
| |
| numeric_array = np.array(array.array(array_type, left._data) ) |
| |
| speech_array = torch.FloatTensor(numeric_array) |
| |
| batch["speech"] = numeric_array |
| batch["sampling_rate"] = sampling_rate |
| #batch["target_text"] = batch["sentence"] |
| |
| return batch |
| |
| |
| # Preprocessing the datasets. |
| # We need to read the aduio files as arrays |
| def speech_file_to_array_fn(batch): |
| batch = audio_resampler(batch, new_sample_rate = new_sample_rate) |
| 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 Turkish test data of Common Voice. |
| ```python |
| import torch |
| import torchaudio |
| from datasets import load_dataset, load_metric |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
| import re |
| import pydub |
| import array |
| import numpy as np |
| |
| test_dataset = load_dataset("common_voice", "tr", split="test") |
| wer = load_metric("wer") |
| processor = Wav2Vec2Processor.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish") |
| model = Wav2Vec2ForCTC.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish") |
| model.to("cuda") |
| |
| #Note: Not ignoring "'" on this one |
| #Note: Not ignoring "'" on this one |
| chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”\\�\\#\\>\\<\\_\\’\\[\\]\\{\\}]' |
| |
| |
| #resampler = torchaudio.transforms.Resample(48_000, 16_000) |
| #using custom load and transformer for audio -> see audio_resampler |
| new_sample_rate = 16000 |
| |
| def audio_resampler(batch, new_sample_rate = 16000): |
| |
| #not working without complex library compilation in windows for mp3 |
| #speech_array, sampling_rate = torchaudio.load(batch["path"]) |
| #speech_array, sampling_rate = librosa.load(batch["path"]) |
| #sampling_rate = pydub.utils.info['sample_rate'] ##gets current samplerate |
| |
| sound = pydub.AudioSegment.from_file(file=batch["path"]) |
| |
| sound = sound.set_frame_rate(new_sample_rate) |
| left = sound.split_to_mono()[0] |
| bit_depth = left.sample_width * 8 |
| array_type = pydub.utils.get_array_type(bit_depth) |
| |
| numeric_array = np.array(array.array(array_type, left._data) ) |
| |
| speech_array = torch.FloatTensor(numeric_array) |
| |
| |
| return speech_array, new_sample_rate |
| |
| def remove_special_characters(batch): |
| |
| ##this one comes from subtitles if additional timestamps not processed -> 00:01:01 00:01:01,33 |
| batch["sentence"] = re.sub('\\b\\d{2}:\\d{2}:\\d{2}(,+\\d{2})?\\b', ' ', batch["sentence"]) |
| ##remove all caps in text [AÇIKLAMA] etc, do it before.. |
| batch["sentence"] = re.sub('\\[(\\b[A-Z]+\\])', '', batch["sentence"]) |
| ##replace three dots (that are inside string with single) |
| batch["sentence"] = re.sub("([a-zA-Z]+)\\.\\.\\.", r"\\1.", batch["sentence"]) |
| #standart ignore list |
| batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " |
| |
| return batch |
| |
| |
| # Preprocessing the datasets. |
| # We need to read the aduio files as arrays |
| new_sample_rate = 16000 |
| 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"]) |
| ##load and conversion done in resampler , takes and returns batch |
| speech_array, sampling_rate = audio_resampler(batch, new_sample_rate = new_sample_rate) |
| batch["speech"] = speech_array |
| batch["sampling_rate"] = sampling_rate |
| batch["target_text"] = batch["sentence"] |
| |
| 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 |
| |
| print("EVALUATING:") |
| |
| ##for 8GB RAM on GPU best is batch_size 2 for windows, 4 may fit in linux only |
| result = test_dataset.map(evaluate, batched=True, batch_size=2) |
| print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
| |
| ``` |
|
|
| **Test Result**: 50.41 % |
|
|
|
|
| ## Training |
|
|
|
|
| The Common Voice `train` and `validation` datasets were used for training. Additional 5 Turkish movies with subtitles also used for training. |
| Similar training model used as base fine-tuning, additional audio resampler is on above code. |
|
|
| Putting model building and merging code below for reference |
|
|
|
|
| ```python |
| import pandas as pd |
| from datasets import load_dataset, load_metric |
| |
| import os |
| from pathlib import Path |
| from datasets import Dataset |
| import csv |
| |
| #Walk all subdirectories of base_set_path and find csv files |
| base_set_path = r'C:\\dataset_extracts' |
| csv_files = [] |
| for path, subdirs, files in os.walk(base_set_path): |
| for name in files: |
| if name.endswith(".csv"): |
| deckfile= os.path.join(path, name) |
| csv_files.append(deckfile) |
| |
| def get_dataset_from_csv_file(csvfilename,names=['sentence', 'path']): |
| path = Path(csvfilename) |
| csv_delimiter="\\t" ##tab seperated, change if something else |
| |
| ##Pandas has bug reading non-ascii file names, make sure use open with encoding |
| df=pd.read_csv(open(path, 'r', encoding='utf-8'), delimiter=csv_delimiter,header=None , names=names, encoding='utf8') |
| return Dataset.from_pandas(df) |
| |
| custom_datasets= [] |
| for csv_file in csv_files: |
| this_dataset=get_dataset_from_csv_file(csv_file) |
| custom_datasets.append(this_dataset) |
| |
| |
| |
| |
| from datasets import concatenate_datasets, load_dataset |
| from datasets import load_from_disk |
| |
| # Merge datasets together (from csv files) |
| dataset_file_path = ".\\dataset_file" |
| custom_datasets_concat = concatenate_datasets( [dset for dset in custom_datasets] ) |
| |
| #save this one to disk |
| custom_datasets_concat.save_to_disk( dataset_file_path ) |
| |
| #load back from disk |
| custom_datasets_from_disk = load_from_disk(dataset_file_path) |
| ``` |
|
|
