Upload ravdess.py

ravdess.py

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+# coding=utf-8
+# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""RAVDESS multimodal dataset for emotion recognition."""
+
+
+import os
+from pathlib import Path, PurePath, PurePosixPath
+from collections import OrderedDict
+import pandas as pd
+import datasets
+
+
+_CITATION = """\
+
+"""
+
+_DESCRIPTION = """\
+
+"""
+
+_URL = "https://zenodo.org/record/1188976/files/Audio_Speech_Actors_01-24.zip"
+_HOMEPAGE = "https://smartlaboratory.org/ravdess/"
+
+_CLASS_NAMES = [
+    'neutral',
+    'calm',
+    'happy', 
+    'sad', 
+    'angry', 
+    'fearful', 
+    'disgust', 
+    'surprised'
+]
+
+
+
+_FEAT_DICT = OrderedDict([
+    ('Modality', ['full-AV', 'video-only', 'audio-only']),
+    ('Vocal channel', ['speech', 'song']),
+    ('Emotion', ['neutral', 'calm', 'happy', 'sad', 'angry', 'fearful', 'disgust', 'surprised']),
+    ('Emotion intensity', ['normal', 'strong']),
+    ('Statement', ["Kids are talking by the door", "Dogs are sitting by the door"]),
+    ('Repetition', ["1st repetition", "2nd repetition"]),
+])
+
+
+def filename2feats(filename):
+    codes = filename.stem.split('-')
+    d = {}
+    for i, k in enumerate(_FEAT_DICT.keys()):
+        d[k] = _FEAT_DICT[k][int(codes[i])-1]
+    d['Actor'] = codes[-1]
+    d['Gender'] = 'female' if int(codes[-1]) % 2 == 0 else 'male'
+    d['Path_to_Wav'] = str(filename)
+    return d
+
+
+def preprocess(data_root_path):
+    output_dir = data_root_path / "RAVDESS_ser"
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    data = []
+    for actor_dir in data_root_path.iterdir():
+        if actor_dir.is_dir() and "Actor" in actor_dir.name:
+            for f in actor_dir.iterdir():
+                data.append(filename2feats(f))
+
+    df = pd.DataFrame(data, columns=list(_FEAT_DICT.keys()) + ['Actor', 'Gender', 'Path_to_Wav'])
+    df.to_csv(output_dir / 'data.csv')
+
+
+
+class RAVDESSConfig(datasets.BuilderConfig):
+    """BuilderConfig for RAVDESS."""
+
+    def __init__(self, **kwargs):
+        """
+        Args:
+          data_dir: `string`, the path to the folder containing the files in the
+            downloaded .tar
+          citation: `string`, citation for the data set
+          url: `string`, url for information about the data set
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(RAVDESSConfig, self).__init__(version=datasets.Version("2.0.1", ""), **kwargs)
+
+
+class RAVDESS(datasets.GeneratorBasedBuilder):
+    """RAVDESS dataset."""
+
+    BUILDER_CONFIGS = [] #RAVDESSConfig(name="clean", description="'Clean' speech.")]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "audio": datasets.Audio(sampling_rate=48000),
+                    "text": datasets.Value("string"),
+                    "labels": datasets.ClassLabel(names=_CLASS_NAMES),
+                    "speaker_id": datasets.Value("string"),
+                    "speaker_gender": datasets.Value("string")
+#                    "id": datasets.Value("string"),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION
+        )
+
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download_and_extract(_URL)
+        archive_path = Path(archive_path)
+        preprocess(archive_path)
+        csv_path = os.path.join(archive_path, "RAVDESS_ser/data.csv")
+
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, 
+                                    gen_kwargs={"data_info_csv": csv_path}),
+        ]
+
+
+    def _generate_examples(self, data_info_csv):
+        print("\nGenerating an example")
+
+        # Read the data info to extract rows mentioning about non-converted audio only
+        data_info = pd.read_csv(open(data_info_csv, encoding="utf8"))
+
+        # Iterating the contents of the data to extract the relevant information
+        for audio_idx in range(data_info.shape[0]):
+            audio_data = data_info.iloc[audio_idx]
+            
+#            subpath = str(audio_data["Path_to_Wav"])
+            # import pathlib
+            # subpath = subpath.replace('\\', '/')
+            # p2 = pathlib.PurePosixPath(subpath)
+            # wav_path = str(pathlib.PurePath(data_path) / p2)
+#            labels = audio_data["Emotion"] #.lower().split(',')
+
+          #  labels = [l for l in labels if len(l) > 1]
+
+            example = {
+                "audio": audio_data['Path_to_Wav'],  #wav_path,
+                "text": audio_data['Statement'],
+                "labels": audio_data['Emotion'],
+                "speaker_id": audio_data["Actor"],
+                "speaker_gender": audio_data["Gender"]
+            }
+
+            yield audio_idx, example
+
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+            
+    # def class_names(self):
+    #     return _CLASS_NAMES
+
+    
+    
+    
+    
+    #            transcript = 
+            # # extract transcript
+            # with open(wav_path.replace(".WAV", ".TXT"), encoding="utf-8") as op:
+            #     transcript = " ".join(op.readlines()[0].split()[2:])  # first two items are sample number