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Update README.md

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  1. README.md +7 -7
README.md CHANGED
@@ -57,8 +57,8 @@ def load_dataset_sundanese():
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  dfs = []
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- dfs.append(pd.read_csv(filenames[0], sep='\\\\t\\\\t', names=["path", "sentence"]))
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- dfs.append(pd.read_csv(filenames[1], sep='\\\\t\\\\t', names=["path", "sentence"]))
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  for i, dir in enumerate(data_dirs):
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  dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
@@ -124,8 +124,8 @@ def load_dataset_sundanese():
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  dfs = []
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- dfs.append(pd.read_csv(filenames[0], sep='\\\\t\\\\t', names=["path", "sentence"]))
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- dfs.append(pd.read_csv(filenames[1], sep='\\\\t\\\\t', names=["path", "sentence"]))
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  for i, dir in enumerate(data_dirs):
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  dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
@@ -145,7 +145,7 @@ processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundane
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  model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
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  model.to("cuda")
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- chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”_\�]'
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  resampler = torchaudio.transforms.Resample(48_000, 16_000)
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  # Preprocessing the datasets.
@@ -181,5 +181,5 @@ print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"],
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  The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
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- The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
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- (will be available soon)
 
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  dfs = []
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+ dfs.append(pd.read_csv(filenames[0], sep='\\\\\\\\t\\\\\\\\t', names=["path", "sentence"]))
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+ dfs.append(pd.read_csv(filenames[1], sep='\\\\\\\\t\\\\\\\\t', names=["path", "sentence"]))
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  for i, dir in enumerate(data_dirs):
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  dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
 
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  dfs = []
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+ dfs.append(pd.read_csv(filenames[0], sep='\\\\\\\\t\\\\\\\\t', names=["path", "sentence"]))
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+ dfs.append(pd.read_csv(filenames[1], sep='\\\\\\\\t\\\\\\\\t', names=["path", "sentence"]))
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  for i, dir in enumerate(data_dirs):
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  dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
 
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  model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
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  model.to("cuda")
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+ chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\'\\”_\\�]'
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  resampler = torchaudio.transforms.Resample(48_000, 16_000)
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  # Preprocessing the datasets.
 
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  The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
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+ The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb)
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+ and to [evaluate it](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb)