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.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+annotations_creators:
+- other
+language:
+- en
+language_creators:
+- other
+license:
+- odc-by
+multilinguality:
+- monolingual
+pretty_name: asvspoof2019
+size_categories:
+- 100K<n<1M
+source_datasets:
+- extended|vctk
+tags: []
+task_categories:
+- audio-classification
+task_ids:
+- voice-anti-spoofing
+---
+
+# Dataset Card for asvspoof2019
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://datashare.ed.ac.uk/handle/10283/3336
+- **Repository:** [Needs More Information]
+- **Paper:** https://arxiv.org/abs/1911.01601
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+This is a database used for the Third Automatic Speaker Verification Spoofing
+and Countermeasuers Challenge, for short, ASVspoof 2019 (http://www.asvspoof.org)
+organized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor
+Delgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman,
+and Andreas Nautsch in 2019.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{'speaker_id': 'LA_0091',
+ 'audio_file_name': 'LA_T_8529430',
+ 'audio': {'path': 'D:/Users/80304531/.cache/huggingface/datasets/downloads/extracted/8cabb6d5c283b0ed94b2219a8d459fea8e972ce098ef14d8e5a97b181f850502/LA/ASVspoof2019_LA_train/flac/LA_T_8529430.flac',
+  'array': array([-0.00201416, -0.00234985, -0.0022583 , ...,  0.01309204,
+          0.01339722,  0.01461792], dtype=float32),
+  'sampling_rate': 16000},
+ 'system_id': 'A01',
+ 'key': 1}
+```
+
+### Data Fields
+
+Logical access (LA):
+- `speaker_id`: `LA_****`, a 4-digit speaker ID
+- `audio_file_name`: name of the audio file
+- `audio`: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+- `system_id`: ID of the speech spoofing system (A01 - A19),  or, for bonafide speech SYSTEM-ID is left blank ('-')
+- `key`: 'bonafide' for genuine speech, or, 'spoof' for spoofing speech
+
+Physical access (PA):
+- `speaker_id`: `PA_****`, a 4-digit speaker ID
+
+- `audio_file_name`: name of the audio file
+
+- `audio`: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
+
+- `environment_id`: a triplet (S,R,D_s), which take one letter in the set {a,b,c} as categorical value, defined as
+
+  |                                  | a      | b       | c        |
+  | -------------------------------- | ------ | ------- | -------- |
+  | S: Room size (square meters)     | 2-5    | 5-10    | 10-20    |
+  | R: T60 (ms)                      | 50-200 | 200-600 | 600-1000 |
+  | D_s: Talker-to-ASV distance (cm) | 10-50  | 50-100  | 100-150  |
+
+- `attack_id`: a duple (D_a,Q), which take one letter in the set {A,B,C} as categorical value, defined as
+
+  |                                     | A       | B      | C     |
+  | ----------------------------------- | ------- | ------ | ----- |
+  | Z: Attacker-to-talker distance (cm) | 10-50   | 50-100 | > 100 |
+  | Q: Replay device quality            | perfect | high   | low   |
+
+  for bonafide speech, `attack_id` is left blank ('-')
+
+- `key`: 'bonafide' for genuine speech, or, 'spoof' for spoofing speech
+
+### Data Splits
+
+|          | Training set | Development set | Evaluation set |
+| -------- | ------------ | --------------- | -------------- |
+| Bonafide | 2580         | 2548            | 7355           |
+| Spoof    | 22800        | 22296           | 63882          |
+| Total    | 25380        | 24844           | 71237          |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+This ASVspoof 2019 dataset is made available under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0/
+
+### Citation Information
+
+```
+@InProceedings{Todisco2019,
+  Title     = {{ASV}spoof 2019: {F}uture {H}orizons in {S}poofed and {F}ake {A}udio {D}etection},
+  Author    = {Todisco, Massimiliano and
+               Wang, Xin and
+               Sahidullah, Md and
+               Delgado, H ́ector and
+               Nautsch, Andreas and
+               Yamagishi, Junichi and
+               Evans, Nicholas and
+               Kinnunen, Tomi and
+               Lee, Kong Aik},
+  booktitle = {Proc. of Interspeech 2019},
+  Year      = {2019}
+}
+```

asvspoof2019.py

@@ -0,0 +1,151 @@
+import os
+
+import datasets
+
+_CITATION = """\
+@InProceedings{Todisco2019,
+  Title     = {{ASV}spoof 2019: {F}uture {H}orizons in {S}poofed and {F}ake {A}udio {D}etection},
+  Author    = {Todisco, Massimiliano and
+               Wang, Xin and
+               Sahidullah, Md and
+               Delgado, H ́ector and
+               Nautsch, Andreas and
+               Yamagishi, Junichi and
+               Evans, Nicholas and
+               Kinnunen, Tomi and
+               Lee, Kong Aik},
+  booktitle = {Proc. of Interspeech 2019},
+  Year      = {2019}
+}
+"""
+
+_DESCRIPTION = """\
+This is a database used for the Third Automatic Speaker Verification Spoofing
+and Countermeasuers Challenge, for short, ASVspoof 2019 (http://www.asvspoof.org)
+organized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor
+Delgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman,
+and Andreas Nautsch in 2019.
+"""
+
+_HOMEPAGE = "https://datashare.ed.ac.uk/handle/10283/3336"
+
+_LICENSE = "http://opendatacommons.org/licenses/by/1.0/"
+
+_URLS = {
+    "LA": "https://datashare.ed.ac.uk/bitstream/handle/10283/3336/LA.zip",
+    "PA": "https://datashare.ed.ac.uk/bitstream/handle/10283/3336/PA.zip",
+}
+
+
+class ASVSpoof2019(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="LA", version=VERSION, description="Logical access (LA)"),
+        datasets.BuilderConfig(name="PA", version=VERSION, description="Physical access (PA)"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "LA"
+
+    def _info(self):
+        if self.config.name == "LA":
+            features = datasets.Features(
+                {
+                    "speaker_id": datasets.Value("string"),
+                    "audio_file_name": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "system_id": datasets.Value("string"),
+                    "key": datasets.ClassLabel(names=["bonafide", "spoof"]),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "speaker_id": datasets.Value("string"),
+                    "audio_file_name": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "environment_id": datasets.Value("string"),
+                    "attack_id": datasets.Value("string"),
+                    "key": datasets.ClassLabel(names=["bonafide", "spoof"]),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("audio", "key"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "metadata_filepath": os.path.join(
+                        data_dir,
+                        self.config.name,
+                        f"ASVspoof2019_{self.config.name}_cm_protocols",
+                        f"ASVspoof2019.{self.config.name}.cm.train.trn.txt",
+                    ),
+                    "audios_dir": os.path.join(
+                        data_dir, self.config.name, f"ASVspoof2019_{self.config.name}_train", "flac"
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "metadata_filepath": os.path.join(
+                        data_dir,
+                        self.config.name,
+                        f"ASVspoof2019_{self.config.name}_cm_protocols",
+                        f"ASVspoof2019.{self.config.name}.cm.dev.trl.txt",
+                    ),
+                    "audios_dir": os.path.join(
+                        data_dir, self.config.name, f"ASVspoof2019_{self.config.name}_dev", "flac"
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "metadata_filepath": os.path.join(
+                        data_dir,
+                        self.config.name,
+                        f"ASVspoof2019_{self.config.name}_cm_protocols",
+                        f"ASVspoof2019.{self.config.name}.cm.eval.trl.txt",
+                    ),
+                    "audios_dir": os.path.join(
+                        data_dir, self.config.name, f"ASVspoof2019_{self.config.name}_eval", "flac"
+                    ),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, metadata_filepath, audios_dir):
+        with open(metadata_filepath) as f:
+            for i, line in enumerate(f.readlines()):
+                if self.config.name == "LA":
+                    speaker_id, audio_file_name, _, system_id, key = line.strip().split()
+                    result = {
+                        "speaker_id": speaker_id,
+                        "audio_file_name": audio_file_name,
+                        "system_id": system_id,
+                        "key": key,
+                    }
+                elif self.config.name == "PA":
+                    speaker_id, audio_file_name, environment_id, attack_id, key = line.strip().split()
+                    result = {
+                        "speaker_id": speaker_id,
+                        "audio_file_name": audio_file_name,
+                        "environment_id": environment_id,
+                        "attack_id": attack_id,
+                        "key": key,
+                    }
+                result["audio"] = os.path.join(audios_dir, audio_file_name + ".flac")
+                yield i, result

dataset_infos.json

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+{"LA": {"description": "This is a database used for the Third Automatic Speaker Verification Spoofing\nand Countermeasuers Challenge, for short, ASVspoof 2019 (http://www.asvspoof.org)\norganized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, H\u00e9ctor\nDelgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman,\nand Andreas Nautsch in 2019.\n", "citation": "@InProceedings{Todisco2019,\n  Title     = {{ASV}spoof 2019: {F}uture {H}orizons in {S}poofed and {F}ake {A}udio {D}etection},\n  Author    = {Todisco, Massimiliano and\n               Wang, Xin and\n               Sahidullah, Md and\n               Delgado, H \u0301ector and\n               Nautsch, Andreas and\n               Yamagishi, Junichi and\n               Evans, Nicholas and\n               Kinnunen, Tomi and\n               Lee, Kong Aik},\n  booktitle = {Proc. of Interspeech 2019},\n  Year      = {2019}\n}\n", "homepage": "https://datashare.ed.ac.uk/handle/10283/3336", "license": "http://opendatacommons.org/licenses/by/1.0/", "features": {"speaker_id": {"dtype": "string", "id": null, "_type": "Value"}, "audio_file_name": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "decode": true, "id": null, "_type": "Audio"}, "system_id": {"dtype": "string", "id": null, "_type": "Value"}, "key": {"num_classes": 2, "names": ["bonafide", "spoof"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "audio", "output": "key"}, "task_templates": null, "builder_name": "asvspoof2019", "config_name": "LA", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5784653, "num_examples": 25380, "dataset_name": "asvspoof2019"}, "validation": {"name": "validation", "num_bytes": 5612754, "num_examples": 24844, "dataset_name": "asvspoof2019"}, "test": {"name": "test", "num_bytes": 16164994, "num_examples": 71237, "dataset_name": "asvspoof2019"}}, "download_checksums": {"https://datashare.ed.ac.uk/bitstream/handle/10283/3336/LA.zip": {"num_bytes": 7640952520, "checksum": "208a7e4e3913f8c75ae1afd19bf32a5b29ae68435e9e30e23e5e98b6a155e4ec"}}, "download_size": 7640952520, "post_processing_size": null, "dataset_size": 27562401, "size_in_bytes": 7668514921}, "PA": {"description": "This is a database used for the Third Automatic Speaker Verification Spoofing\nand Countermeasuers Challenge, for short, ASVspoof 2019 (http://www.asvspoof.org)\norganized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, H\u00e9ctor\nDelgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman,\nand Andreas Nautsch in 2019.\n", "citation": "@InProceedings{Todisco2019,\n  Title     = {{ASV}spoof 2019: {F}uture {H}orizons in {S}poofed and {F}ake {A}udio {D}etection},\n  Author    = {Todisco, Massimiliano and\n               Wang, Xin and\n               Sahidullah, Md and\n               Delgado, H \u0301ector and\n               Nautsch, Andreas and\n               Yamagishi, Junichi and\n               Evans, Nicholas and\n               Kinnunen, Tomi and\n               Lee, Kong Aik},\n  booktitle = {Proc. of Interspeech 2019},\n  Year      = {2019}\n}\n", "homepage": "https://datashare.ed.ac.uk/handle/10283/3336", "license": "http://opendatacommons.org/licenses/by/1.0/", "features": {"speaker_id": {"dtype": "string", "id": null, "_type": "Value"}, "audio_file_name": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "decode": true, "id": null, "_type": "Audio"}, "environment_id": {"dtype": "string", "id": null, "_type": "Value"}, "attack_id": {"dtype": "string", "id": null, "_type": "Value"}, "key": {"num_classes": 2, "names": ["bonafide", "spoof"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "audio", "output": "key"}, "task_templates": null, "builder_name": "asvspoof2019", "config_name": "PA", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 12637350, "num_examples": 54000, "dataset_name": "asvspoof2019"}, "validation": {"name": "validation", "num_bytes": 6888713, "num_examples": 29700, "dataset_name": "asvspoof2019"}, "test": {"name": "test", "num_bytes": 31390842, "num_examples": 134730, "dataset_name": "asvspoof2019"}}, "download_checksums": {"https://datashare.ed.ac.uk/bitstream/handle/10283/3336/PA.zip": {"num_bytes": 17662711934, "checksum": "cb2a2d1bd37527177be6f259339cb3a7558638474e150bf5b086c37d26daad2a"}}, "download_size": 17662711934, "post_processing_size": null, "dataset_size": 50916905, "size_in_bytes": 17713628839}}

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