Update ESLO_text_only.py

ESLO_text_only.py

@@ -1,13 +1,10 @@
 import os
 import re
-from ctypes import Array
 from dataclasses import dataclass
 from typing import List
 import xml.etree.ElementTree as ET
-import ffmpeg
 
 import datasets
-import numpy as np
 
 _CITATION = """\
 @misc{11403/eslo/v1,
@@ -23,8 +20,6 @@ _CITATION = """\
 _DESCRIPTION = """\
 ESLO dataset, each utterance are taken out individually
 """
-SAMPLING_RATE = 16000
-AUDIO_FOLDER = "audio"
 TEST_TRANSCRIPTS = ['ESLO2_ENT_1052_C.trs', 'ESLO1_ENT_026_C.trs', 'ESLO2_ECOLE_1280_C.trs', 'ESLO1_CONSCMPP_727_C.trs',
                     'ESLO1_ENT_002_C.trs', 'ESLO1_TEL_338_C.trs', 'ESLO1_ENTCONT_213_C.trs', 'ESLO1_INTPERS_449_C.trs',
                     'ESLO1_CONSCMPP_740_C.trs', 'ESLO2_ENT_1008_C.trs', 'ESLO2_REPAS_1268_C.trs',
@@ -236,10 +231,6 @@ class ESLOConfig(datasets.BuilderConfig):
         super(ESLOConfig, self).__init__(
             version=datasets.Version("2.11.0", ""), name=name, **kwargs
         )
-        if "no_overlap" in name:
-            self.overlap = False
-        else:
-            self.overlap = True
         if "no_hesitation" in name:
             self.hesitation = False
         else:
@@ -250,14 +241,11 @@ class ESLO(datasets.GeneratorBasedBuilder):
     """ESLO dataset."""
 
     BUILDER_CONFIGS = [
-        ESLOConfig(name="no_overlap_no_hesitation", description="ESLO dataset, removed hesitations from samples"
-                                                                " and all samples with overlap"),
         ESLOConfig(name="no_hesitation", description="ESLO dataset, removed hesitations from samples"),
-        ESLOConfig(name="no_overlap", description="ESLO dataset, removed all samples with overlap"),
         ESLOConfig(name="raw", description="ESLO dataset"),
     ]
 
-    DEFAULT_CONFIG_NAME = "no_overlap_no_hesitation"
+    DEFAULT_CONFIG_NAME = "raw"
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -265,7 +253,6 @@ class ESLO(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "file": datasets.Value("string"),
-                    "audio": datasets.features.Audio(sampling_rate=SAMPLING_RATE),
                     "sentence": datasets.Value("string"),
                     "speaker": datasets.Value("string"),
                     "start_timestamp": datasets.Value("float"),
@@ -283,26 +270,18 @@ class ESLO(datasets.GeneratorBasedBuilder):
         transcripts_test = dl_manager.download(
             [os.path.join("transcripts_deduplicated_test", transcript) for transcript in
              TEST_TRANSCRIPTS])
-        train_filenames = [os.path.splitext(transcript)[0] for transcript in TRAIN_TRANSCRIPTS]
-        test_filenames = [os.path.splitext(transcript)[0] for transcript in TEST_TRANSCRIPTS]
-        audio_train = dl_manager.download(
-            {transcript: f"audio/{transcript[:-2]}.mp4" for transcript in train_filenames})
-        audio_test = dl_manager.download(
-            {transcript: f"audio/{transcript[:-2]}.mp4" for transcript in test_filenames})
 
         splits = [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
                     "transcripts": transcripts_train,
-                    "audio_files": audio_train
                 }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
                     "transcripts": transcripts_test,
-                    "audio_files": audio_test
                 }
             ),
 
@@ -355,40 +334,11 @@ class ESLO(datasets.GeneratorBasedBuilder):
                 ))
         return utts
 
-    @staticmethod
-    def load_audio(file: str, sr: int = SAMPLING_RATE):
-        """
-        Open an audio file and read as mono waveform, resampling as necessary
-        Parameters
-        ----------
-        file:vThe audio file to read
-        sr: int
-            The sample rate to resample the audio if necessary
-        Returns
-        -------
-        A NumPy array containing the audio waveform, in float32 dtype.
-        """
-        try:
-            # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
-            # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
-            out, _ = (
-                ffmpeg.input(file)
-                .output('-', format='s16le', acodec='pcm_s16le', ac=1, ar=sr)
-                .run(capture_stdout=True, capture_stderr=True)
-            )
-        except ffmpeg.Error as e:
-            raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-        return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
-
-    @staticmethod
-    def _cut_audio(audio: Array, start_timestamp: float, end_timestamp: float):
-        return audio[int(round(start_timestamp * SAMPLING_RATE)): int(round(end_timestamp * SAMPLING_RATE)) + 1]
 
-    def _generate_examples(self, transcripts, audio_files):
+    def _generate_examples(self, transcripts):
         """Generate examples from a Multilingual LibriSpeech data dir."""
         for path in transcripts:
             transcript_name = os.path.splitext(os.path.basename(path))[0]
-            audio = self.load_audio(audio_files[transcript_name])
             with open(path, "rb") as file:
                 for utterance in self.load_one(file):
                     if not self.config.overlap and utterance.overlap:
@@ -399,7 +349,4 @@ class ESLO(datasets.GeneratorBasedBuilder):
                         "start_timestamp": utterance.start_timestamp,
                         "end_timestamp": utterance.end_timestamp,
                         "speaker": utterance.speaker,
-                        "overlap": utterance.overlap,
-                        "audio": {"path": transcript_name,
-                                  "array": self._cut_audio(audio, utterance.start_timestamp, utterance.end_timestamp),
-                                  "sampling_rate": 16000}}
+                        "overlap": utterance.overlap}