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import os |
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import logging |
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from speechbrain.dataio.dataio import read_audio |
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try: |
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import pandas as pd |
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except ImportError: |
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err_msg = ( |
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"The optional dependency pandas must be installed to run this recipe.\n" |
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) |
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err_msg += "Install using `pip install pandas`.\n" |
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raise ImportError(err_msg) |
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logger = logging.getLogger(__name__) |
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def prepare_FSC(data_folder, save_folder, skip_prep=False): |
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""" |
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This function prepares the Fluent Speech Commands dataset. |
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data_folder : path to dataset. |
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save_folder: folder where the manifest files will be stored. |
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skip_prep: If True, skip data preparation |
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""" |
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if skip_prep: |
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return |
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splits = [ |
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"train", |
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"valid", |
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"test", |
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] |
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ID_start = 0 |
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for split in splits: |
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new_filename = os.path.join(save_folder, split) + ".csv" |
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if os.path.exists(new_filename): |
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continue |
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logger.info("Preparing %s..." % new_filename) |
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ID = [] |
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duration = [] |
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wav = [] |
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wav_format = [] |
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wav_opts = [] |
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spk_id = [] |
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spk_id_format = [] |
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spk_id_opts = [] |
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semantics = [] |
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semantics_format = [] |
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semantics_opts = [] |
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transcript = [] |
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transcript_format = [] |
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transcript_opts = [] |
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df = pd.read_csv(os.path.join(data_folder, "data", split) + "_data.csv") |
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for i in range(len(df)): |
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ID.append(ID_start + i) |
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signal = read_audio(os.path.join(data_folder, df.path[i])) |
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duration.append(signal.shape[0] / 16000) |
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wav.append(os.path.join(data_folder, df.path[i])) |
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wav_format.append("wav") |
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wav_opts.append(None) |
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spk_id.append(df.speakerId[i]) |
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spk_id_format.append("string") |
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spk_id_opts.append(None) |
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transcript_ = df.transcription[i] |
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transcript.append(transcript_) |
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transcript_format.append("string") |
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transcript_opts.append(None) |
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semantics_ = ( |
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'{"action:" "' |
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+ df.action[i] |
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+ '"| "object": "' |
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+ df.object[i] |
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+ '"| "location": "' |
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+ df.location[i] |
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+ '"}' |
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) |
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semantics.append(semantics_) |
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semantics_format.append("string") |
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semantics_opts.append(None) |
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new_df = pd.DataFrame( |
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{ |
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"ID": ID, |
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"duration": duration, |
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"wav": wav, |
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"spk_id": spk_id, |
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"semantics": semantics, |
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"transcript": transcript, |
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} |
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) |
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new_df.to_csv(new_filename, index=False) |
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ID_start += len(df) |
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