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Partition_10_tsv/calibration/calibration.py

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+from collections import defaultdict
+import os
+import json
+import csv
+
+import datasets
+
+_NAME="spanish_trans_uq"
+_VERSION="1.0.0"
+_AUDIO_EXTENSIONS=".wav"
+
+_DESCRIPTION = """
+A custom dataset to evaluate UQ methods
+
+"""
+
+_CITATION = """
+TODO
+"""
+
+_HOMEPAGE = "todo"
+
+_LICENSE = "CC-BY-SA-4.0, See https://creativecommons.org/licenses/by-sa/4.0/"
+
+_BASE_DATA_DIR = "corpus/"
+_METADATA_calibration  =  os.path.join(_BASE_DATA_DIR,"files", "metadata_calibration.tsv")
+
+_TARS_calibration  = os.path.join(_BASE_DATA_DIR,"files", "tars_calibration.paths")
+
+class SpanishTransUQcalibrationConfig(datasets.BuilderConfig):
+    """BuilderConfig for the Spanish Transcription Uncertainty Quantification Benchmark Dataset"""
+
+    def __init__(self, name, **kwargs):
+        name=_NAME
+        super().__init__(name=name, **kwargs)
+
+class SpanishTransUQcalibrationConfig(datasets.GeneratorBasedBuilder):
+    """for the Spanish Transcription Uncertainty Quantification Benchmark Dataset"""
+
+    VERSION = datasets.Version(_VERSION)
+    BUILDER_CONFIGS = [
+        SpanishTransUQcalibrationConfig(
+            name=_NAME,
+            version=datasets.Version(_VERSION),
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio_id": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16000),              
+                "normalized_text": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        metadata_calibration=dl_manager.download_and_extract(_METADATA_calibration)
+        
+        tars_calibration=dl_manager.download_and_extract(_TARS_calibration)
+        
+        hash_tar_files=defaultdict(dict)
+
+        with open(tars_calibration,'r') as f:
+            hash_tar_files['calibration']=[path.replace('\n','') for path in f]
+
+        hash_meta_paths={"calibration":metadata_calibration}
+        audio_paths = dl_manager.download(hash_tar_files)
+        
+        splits=["calibration"]
+        local_extracted_audio_paths = (
+            dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
+            {
+                split:[None] * len(audio_paths[split]) for split in splits
+            }
+        )                                                                                                            
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.calibration,
+                gen_kwargs={
+                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["calibration"]],
+                    "local_extracted_archives_paths": local_extracted_audio_paths["calibration"],
+                    "metadata_paths": hash_meta_paths["calibration"],
+                }
+            ),
+        ]
+
+    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
+
+        features = ["speaker_id","gender","duration","normalized_text"]
+        
+        with open(metadata_paths) as f:
+            metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}
+
+        for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
+            for audio_filename, audio_file in audio_archive:
+                #audio_id = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[0]
+                audio_id =os.path.splitext(os.path.basename(audio_filename))[0]
+                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
+                                        
+                yield audio_id, {
+                    "audio_id": audio_id,
+                    **{feature: metadata[audio_id][feature] for feature in features},
+                    "audio": {"path": path, "bytes": audio_file.read()},
+                }

Partition_10_tsv/calibration/corpus/files/tars_calibration.paths

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+corpus/speech/calibration.tar.gz

Partition_10_tsv/calibration/corpus/speech/calibration.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2ed6d3fed72d1d9931eb0477120b82f891c693730296ced600c3bea16e737313
+size 24737792

Partition_10_tsv/fine_tune/corpus/files/tars_fine_tune.paths

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+corpus/speech/fine_tune.tar.gz

Partition_10_tsv/fine_tune/corpus/speech/fine_tune.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c619a63a022b586bc9e172a8b1924e03b648941863b9e61a16d54de12e59b109
+size 75936768

Partition_10_tsv/fine_tune/fine_tune.py

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+from collections import defaultdict
+import os
+import json
+import csv
+
+import datasets
+
+_NAME="spanish_trans_uq"
+_VERSION="1.0.0"
+_AUDIO_EXTENSIONS=".wav"
+
+_DESCRIPTION = """
+A custom dataset to evaluate UQ methods
+
+"""
+
+_CITATION = """
+TODO
+"""
+
+_HOMEPAGE = "todo"
+
+_LICENSE = "CC-BY-SA-4.0, See https://creativecommons.org/licenses/by-sa/4.0/"
+
+_BASE_DATA_DIR = "corpus/"
+_METADATA_fine_tune  =  os.path.join(_BASE_DATA_DIR,"files", "metadata_fine_tune.tsv")
+
+_TARS_fine_tune  = os.path.join(_BASE_DATA_DIR,"files", "tars_fine_tune.paths")
+
+class SpanishTransUQfine_tuneConfig(datasets.BuilderConfig):
+    """BuilderConfig for the Spanish Transcription Uncertainty Quantification Benchmark Dataset"""
+
+    def __init__(self, name, **kwargs):
+        name=_NAME
+        super().__init__(name=name, **kwargs)
+
+class SpanishTransUQfine_tuneConfig(datasets.GeneratorBasedBuilder):
+    """for the Spanish Transcription Uncertainty Quantification Benchmark Dataset"""
+
+    VERSION = datasets.Version(_VERSION)
+    BUILDER_CONFIGS = [
+        SpanishTransUQfine_tuneConfig(
+            name=_NAME,
+            version=datasets.Version(_VERSION),
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio_id": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16000),              
+                "normalized_text": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        metadata_fine_tune=dl_manager.download_and_extract(_METADATA_fine_tune)
+        
+        tars_fine_tune=dl_manager.download_and_extract(_TARS_fine_tune)
+        
+        hash_tar_files=defaultdict(dict)
+
+        with open(tars_fine_tune,'r') as f:
+            hash_tar_files['fine_tune']=[path.replace('\n','') for path in f]
+
+        hash_meta_paths={"fine_tune":metadata_fine_tune}
+        audio_paths = dl_manager.download(hash_tar_files)
+        
+        splits=["fine_tune"]
+        local_extracted_audio_paths = (
+            dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
+            {
+                split:[None] * len(audio_paths[split]) for split in splits
+            }
+        )                                                                                                            
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.fine_tune,
+                gen_kwargs={
+                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["fine_tune"]],
+                    "local_extracted_archives_paths": local_extracted_audio_paths["fine_tune"],
+                    "metadata_paths": hash_meta_paths["fine_tune"],
+                }
+            ),
+        ]
+
+    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
+
+        features = ["speaker_id","gender","duration","normalized_text"]
+        
+        with open(metadata_paths) as f:
+            metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}
+
+        for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
+            for audio_filename, audio_file in audio_archive:
+                #audio_id = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[0]
+                audio_id =os.path.splitext(os.path.basename(audio_filename))[0]
+                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
+                                        
+                yield audio_id, {
+                    "audio_id": audio_id,
+                    **{feature: metadata[audio_id][feature] for feature in features},
+                    "audio": {"path": path, "bytes": audio_file.read()},
+                }

Partition_10_tsv/test/corpus/files/tars_test.paths

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+corpus/speech/test.tar.gz

Partition_10_tsv/test/corpus/speech/test.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9359ae6ab67a9b12f7de9961ea178d294deacfd8ed03a695c91a06fd0a8ad261
+size 26424832

Partition_10_tsv/test/test.py

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+from collections import defaultdict
+import os
+import json
+import csv
+
+import datasets
+
+_NAME="spanish_trans_uq"
+_VERSION="1.0.0"
+_AUDIO_EXTENSIONS=".wav"
+
+_DESCRIPTION = """
+A custom dataset to evaluate UQ methods
+
+"""
+
+_CITATION = """
+TODO
+"""
+
+_HOMEPAGE = "todo"
+
+_LICENSE = "CC-BY-SA-4.0, See https://creativecommons.org/licenses/by-sa/4.0/"
+
+_BASE_DATA_DIR = "corpus/"
+_METADATA_TEST  =  os.path.join(_BASE_DATA_DIR,"files", "metadata_test.tsv")
+
+_TARS_TEST  = os.path.join(_BASE_DATA_DIR,"files", "tars_test.paths")
+
+class SpanishTransUQTestConfig(datasets.BuilderConfig):
+    """BuilderConfig for the Spanish Transcription Uncertainty Quantification Benchmark Dataset"""
+
+    def __init__(self, name, **kwargs):
+        name=_NAME
+        super().__init__(name=name, **kwargs)
+
+class SpanishTransUQTestConfig(datasets.GeneratorBasedBuilder):
+    """for the Spanish Transcription Uncertainty Quantification Benchmark Dataset"""
+
+    VERSION = datasets.Version(_VERSION)
+    BUILDER_CONFIGS = [
+        SpanishTransUQTestConfig(
+            name=_NAME,
+            version=datasets.Version(_VERSION),
+        )
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio_id": datasets.Value("string"),
+                "audio": datasets.Audio(sampling_rate=16000),              
+                "normalized_text": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        metadata_test=dl_manager.download_and_extract(_METADATA_TEST)
+        
+        tars_test=dl_manager.download_and_extract(_TARS_TEST)
+        
+        hash_tar_files=defaultdict(dict)
+
+        with open(tars_test,'r') as f:
+            hash_tar_files['test']=[path.replace('\n','') for path in f]
+
+        hash_meta_paths={"test":metadata_test}
+        audio_paths = dl_manager.download(hash_tar_files)
+        
+        splits=["test"]
+        local_extracted_audio_paths = (
+            dl_manager.extract(audio_paths) if not dl_manager.is_streaming else
+            {
+                split:[None] * len(audio_paths[split]) for split in splits
+            }
+        )                                                                                                            
+                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_paths["test"]],
+                    "local_extracted_archives_paths": local_extracted_audio_paths["test"],
+                    "metadata_paths": hash_meta_paths["test"],
+                }
+            ),
+        ]
+
+    def _generate_examples(self, audio_archives, local_extracted_archives_paths, metadata_paths):
+
+        features = ["speaker_id","gender","duration","normalized_text"]
+        
+        with open(metadata_paths) as f:
+            metadata = {x["audio_id"]: x for x in csv.DictReader(f, delimiter="\t")}
+
+        for audio_archive, local_extracted_archive_path in zip(audio_archives, local_extracted_archives_paths):
+            for audio_filename, audio_file in audio_archive:
+                #audio_id = audio_filename.split(os.sep)[-1].split(_AUDIO_EXTENSIONS)[0]
+                audio_id =os.path.splitext(os.path.basename(audio_filename))[0]
+                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path else audio_filename
+                                        
+                yield audio_id, {
+                    "audio_id": audio_id,
+                    **{feature: metadata[audio_id][feature] for feature in features},
+                    "audio": {"path": path, "bytes": audio_file.read()},
+                }