--- 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 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, its recognition (with some letter errors) seems better. 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) ```