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--- |
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language: |
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- tr |
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datasets: |
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- common_voice |
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metrics: |
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- wer |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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- xlsr-fine-tuning-week |
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license: apache-2.0 |
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model-index: |
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- name: XLSR Wav2Vec2 Large Turkish by Gorkem Goknar |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Common Voice tr |
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type: common_voice |
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args: tr |
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metrics: |
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- name: Test WER |
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type: wer |
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value: TBD |
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--- |
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# Wav2Vec2-Large-XLSR-53-Turkish |
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Note: Common voice Turkish data is no background noise voice only, slower than usual day speech dataset. |
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In this model although Word Error rate for test is 50% it is agains Common Voice text. |
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Please try speech yourself and see it is converting pretty good . |
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I hope some news channels or movie producers lets use their data for test/training (I asked some no reply) |
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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). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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import torch |
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import torchaudio |
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import pydub |
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from pydub.utils import mediainfo |
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import array |
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from pydub import AudioSegment |
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from pydub.utils import get_array_type |
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import numpy as np |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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test_dataset = load_dataset("common_voice", "tr", split="test[:2%]") |
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processor = Wav2Vec2Processor.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish") |
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model = Wav2Vec2ForCTC.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish") |
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new_sample_rate = 16000 |
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def audio_resampler(batch, new_sample_rate = 16000): |
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#not working without complex library compilation in windows for mp3 |
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#speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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#speech_array, sampling_rate = librosa.load(batch["path"]) |
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#sampling_rate = pydub.utils.info['sample_rate'] ##gets current samplerate |
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sound = pydub.AudioSegment.from_file(file=batch["path"]) |
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sampling_rate = new_sample_rate |
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sound = sound.set_frame_rate(new_sample_rate) |
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left = sound.split_to_mono()[0] |
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bit_depth = left.sample_width * 8 |
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array_type = pydub.utils.get_array_type(bit_depth) |
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numeric_array = np.array(array.array(array_type, left._data) ) |
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speech_array = torch.FloatTensor(numeric_array) |
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batch["speech"] = numeric_array |
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batch["sampling_rate"] = sampling_rate |
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#batch["target_text"] = batch["sentence"] |
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return batch |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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batch = audio_resampler(batch, new_sample_rate = new_sample_rate) |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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print("Prediction:", processor.batch_decode(predicted_ids)) |
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print("Reference:", test_dataset["sentence"][:2]) |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the Turkish test data of Common Voice. |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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import pydub |
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import array |
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import numpy as np |
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test_dataset = load_dataset("common_voice", "tr", split="test") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish") |
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model = Wav2Vec2ForCTC.from_pretrained("gorkemgoknar/wav2vec2-large-xlsr-53-turkish") |
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model.to("cuda") |
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#Note: Not ignoring "'" on this one |
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#Note: Not ignoring "'" on this one |
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chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�\#\>\<\_\’\[\]\{\}]' |
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#resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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#using custom load and transformer for audio -> see audio_resampler |
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new_sample_rate = 16000 |
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def audio_resampler(batch, new_sample_rate = 16000): |
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#not working without complex library compilation in windows for mp3 |
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#speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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#speech_array, sampling_rate = librosa.load(batch["path"]) |
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#sampling_rate = pydub.utils.info['sample_rate'] ##gets current samplerate |
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sound = pydub.AudioSegment.from_file(file=batch["path"]) |
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sampling_rate = new_sample_rate |
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sound = sound.set_frame_rate(new_sample_rate) |
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left = sound.split_to_mono()[0] |
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bit_depth = left.sample_width * 8 |
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array_type = pydub.utils.get_array_type(bit_depth) |
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numeric_array = np.array(array.array(array_type, left._data) ) |
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speech_array = torch.FloatTensor(numeric_array) |
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batch["speech"] = numeric_array |
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batch["sampling_rate"] = sampling_rate |
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#batch["target_text"] = batch["sentence"] |
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return batch |
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def remove_special_characters(batch): |
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##this one comes from subtitles if additional timestamps not processed -> 00:01:01 00:01:01,33 |
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batch["sentence"] = re.sub('\b\d{2}:\d{2}:\d{2}(,+\d{2})?\b', ' ', batch["sentence"]) |
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##remove all caps in text [AÇIKLAMA] etc, do it before.. |
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batch["sentence"] = re.sub('\[(\b[A-Z]+\])', '', batch["sentence"]) |
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##replace three dots (that are inside string with single) |
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batch["sentence"] = re.sub("([a-zA-Z]+)\.\.\.", r"\1.", batch["sentence"]) |
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#standart ignore list |
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " |
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return batch |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() |
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##speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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##load and conversion done in resampler , takes and returns batch |
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batch = audio_resampler(batch, new_sample_rate = new_sample_rate) |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def evaluate(batch): |
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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print("EVALUATING:") |
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##for 8GB RAM on GPU best is batch_size 2 for windows, 4 may fit in linux only |
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result = test_dataset.map(evaluate, batched=True, batch_size=2) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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**Test Result**: 50.41 % |
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## Training |
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The Common Voice `train` and `validation` datasets were used for training. Additional 5 Turkish movies with subtitles also used. |
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Training still continues... |
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