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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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- movies |
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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 with extended dataset 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: 50.41 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Turkish |
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Note: This model is trained with 5 Turkish movies additional to common voice dataset. |
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Although WER is high (50%) per common voice test dataset, performance from "other sources " seems pretty good. |
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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. |
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Dataset building from csv and merging code can be found on below of this Readme. |
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Please try speech yourself on the right side to see its performance. |
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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) and 5 Turkish movies that include background noise/talkers . |
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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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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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return speech_array, new_sample_rate |
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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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new_sample_rate = 16000 |
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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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speech_array, sampling_rate = audio_resampler(batch, new_sample_rate = new_sample_rate) |
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batch["speech"] = speech_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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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 for training. |
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Similar training model used as base fine-tuning, additional audio resampler is on above code. |
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Putting model building and merging code below for reference |
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```python |
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import pandas as pd |
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from datasets import load_dataset, load_metric |
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import os |
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from pathlib import Path |
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from datasets import Dataset |
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import csv |
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#Walk all subdirectories of base_set_path and find csv files |
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base_set_path = r'C:\\dataset_extracts' |
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csv_files = [] |
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for path, subdirs, files in os.walk(base_set_path): |
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for name in files: |
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if name.endswith(".csv"): |
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deckfile= os.path.join(path, name) |
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csv_files.append(deckfile) |
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def get_dataset_from_csv_file(csvfilename,names=['sentence', 'path']): |
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path = Path(csvfilename) |
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csv_delimiter="\\t" ##tab seperated, change if something else |
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##Pandas has bug reading non-ascii file names, make sure use open with encoding |
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df=pd.read_csv(open(path, 'r', encoding='utf-8'), delimiter=csv_delimiter,header=None , names=names, encoding='utf8') |
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return Dataset.from_pandas(df) |
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custom_datasets= [] |
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for csv_file in csv_files: |
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this_dataset=get_dataset_from_csv_file(csv_file) |
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custom_datasets.append(this_dataset) |
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from datasets import concatenate_datasets, load_dataset |
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from datasets import load_from_disk |
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# Merge datasets together (from csv files) |
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dataset_file_path = ".\\dataset_file" |
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custom_datasets_concat = concatenate_datasets( [dset for dset in custom_datasets] ) |
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#save this one to disk |
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custom_datasets_concat.save_to_disk( dataset_file_path ) |
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#load back from disk |
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custom_datasets_from_disk = load_from_disk(dataset_file_path) |
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``` |
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