--- language: - tr datasets: - common_voice 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: TBD --- # Wav2Vec2-Large-XLSR-53-Turkish Note: Common voice Turkish data is no background noise voice only dataset In this model although Word Error rate for test is 50% it is agains Common Voice text Please try speech yourself and see it is converting pretty good I hope some news channels or movie producers lets use their data for test/training (I asked some no reply) 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). 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"]) 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 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 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 batch = audio_resampler(batch, new_sample_rate = new_sample_rate) 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. Training still continues...