mls_without_script (#15)

- Upload dataset (56cb16aa5347685f0059abd744f098c2e0716eb1)
- Upload dataset (b2688928afd3a23afaca076626ee58346feef469)
- Upload dataset (c82617d933b9de71a3ceebce12cc2d8da66bc319)
- Upload dataset (8ac09f12ed9cc3a6676c13be759a02de56af3eea)
- Upload dataset (part 00000-of-00002) (124f036150ad37664355f25c1cb79988b674f199)
- Upload dataset (part 00001-of-00002) (12bf0eb4b1e903aafdb0a4c39f2353872e07d8f6)
- Upload dataset (0d33592b032b0c6d9c8fc2530bc7f48147f7b9d9)
- Upload dataset (part 00000-of-00002) (0a4a1bac8fbb68632722ced68b81645c1faec46b)
- Upload dataset (part 00001-of-00002) (c22a1da6206b7a069e9d8d1e69ac423b771e560d)
- Update README.md (326dcbdd5fd51afc48fc11d276991c2b404317c5)
- Create create_dataset.py (8a439ef1cea73b319377c4f688a691359c91017f)
- Delete multilingual_librispeech.py (314c28d8ff4b9546bd8e1c956731ece5e6984158)


Co-authored-by: Yoach Lacombe <[email protected]>

README.md

@@ -1,5 +1,4 @@
 ---
-pretty_name: MultiLingual LibriSpeech
 annotations_creators:
 - expert-generated
 language_creators:
@@ -13,17 +12,387 @@ language:
 - es
 - pt
 - pl
+- en
 license:
 - cc-by-4.0
 multilinguality:
 - multilingual
-paperswithcode_id: multilingual-librispeech
 size_categories:
 - 100K<n<1M
 source_datasets:
 - original
 task_categories:
 - automatic-speech-recognition
+- text-to-speech
+- text-to-audio
+paperswithcode_id: multilingual-librispeech
+pretty_name: MultiLingual LibriSpeech
+dataset_info:
+- config_name: dutch
+  features:
+  - name: audio
+    dtype: audio
+  - name: original_path
+    dtype: string
+  - name: begin_time
+    dtype: float64
+  - name: end_time
+    dtype: float64
+  - name: transcript
+    dtype: string
+  - name: audio_duration
+    dtype: float64
+  - name: speaker_id
+    dtype: string
+  - name: chapter_id
+    dtype: string
+  - name: file
+    dtype: string
+  - name: id
+    dtype: string
+  splits:
+  - name: dev
+    num_bytes: 199959986
+    num_examples: 3095
+  - name: test
+    num_bytes: 199298575
+    num_examples: 3075
+  - name: train
+    num_bytes: 23931679031
+    num_examples: 374287
+  - name: 9_hours
+    num_bytes: 139884664.668
+    num_examples: 2153
+  - name: 1_hours
+    num_bytes: 15462181
+    num_examples: 234
+  download_size: 24376256629
+  dataset_size: 24486284437.668
+- config_name: french
+  features:
+  - name: audio
+    dtype: audio
+  - name: original_path
+    dtype: string
+  - name: begin_time
+    dtype: float64
+  - name: end_time
+    dtype: float64
+  - name: transcript
+    dtype: string
+  - name: audio_duration
+    dtype: float64
+  - name: speaker_id
+    dtype: string
+  - name: chapter_id
+    dtype: string
+  - name: file
+    dtype: string
+  - name: id
+    dtype: string
+  splits:
+  - name: dev
+    num_bytes: 157923970.696
+    num_examples: 2416
+  - name: test
+    num_bytes: 158352158.582
+    num_examples: 2426
+  - name: train
+    num_bytes: 16984935842.04
+    num_examples: 258213
+  - name: 9_hours
+    num_bytes: 142796680.609
+    num_examples: 2167
+  - name: 1_hours
+    num_bytes: 15675831
+    num_examples: 241
+  download_size: 17381581776
+  dataset_size: 17459684482.927002
+- config_name: german
+  features:
+  - name: audio
+    dtype: audio
+  - name: original_path
+    dtype: string
+  - name: begin_time
+    dtype: float64
+  - name: end_time
+    dtype: float64
+  - name: transcript
+    dtype: string
+  - name: audio_duration
+    dtype: float64
+  - name: speaker_id
+    dtype: string
+  - name: chapter_id
+    dtype: string
+  - name: file
+    dtype: string
+  - name: id
+    dtype: string
+  splits:
+  - name: dev
+    num_bytes: 224293581.302
+    num_examples: 3469
+  - name: test
+    num_bytes: 225756069.096
+    num_examples: 3394
+  - name: train
+    num_bytes: 31050881388
+    num_examples: 469942
+  - name: 9_hours
+    num_bytes: 142777983.118
+    num_examples: 2194
+  - name: 1_hours
+    num_bytes: 15714704
+    num_examples: 241
+  download_size: 31526161821
+  dataset_size: 31659423725.516
+- config_name: italian
+  features:
+  - name: audio
+    dtype: audio
+  - name: original_path
+    dtype: string
+  - name: begin_time
+    dtype: float64
+  - name: end_time
+    dtype: float64
+  - name: transcript
+    dtype: string
+  - name: audio_duration
+    dtype: float64
+  - name: speaker_id
+    dtype: string
+  - name: chapter_id
+    dtype: string
+  - name: file
+    dtype: string
+  - name: id
+    dtype: string
+  splits:
+  - name: dev
+    num_bytes: 81607596.048
+    num_examples: 1248
+  - name: test
+    num_bytes: 83216752.046
+    num_examples: 1262
+  - name: train
+    num_bytes: 3896742625
+    num_examples: 59623
+  - name: 9_hours
+    num_bytes: 141671904.428
+    num_examples: 2173
+  - name: 1_hours
+    num_bytes: 15560398
+    num_examples: 240
+  download_size: 4200633596
+  dataset_size: 4218799275.522
+- config_name: polish
+  features:
+  - name: audio
+    dtype: audio
+  - name: original_path
+    dtype: string
+  - name: begin_time
+    dtype: float64
+  - name: end_time
+    dtype: float64
+  - name: transcript
+    dtype: string
+  - name: audio_duration
+    dtype: float64
+  - name: speaker_id
+    dtype: string
+  - name: chapter_id
+    dtype: string
+  - name: file
+    dtype: string
+  - name: id
+    dtype: string
+  splits:
+  - name: dev
+    num_bytes: 32746725
+    num_examples: 512
+  - name: test
+    num_bytes: 33735044
+    num_examples: 520
+  - name: train
+    num_bytes: 1638889846
+    num_examples: 25043
+  - name: 9_hours
+    num_bytes: 142005461
+    num_examples: 2173
+  - name: 1_hours
+    num_bytes: 15681216
+    num_examples: 238
+  download_size: 1855342312
+  dataset_size: 1863058292
+- config_name: portuguese
+  features:
+  - name: audio
+    dtype: audio
+  - name: original_path
+    dtype: string
+  - name: begin_time
+    dtype: float64
+  - name: end_time
+    dtype: float64
+  - name: transcript
+    dtype: string
+  - name: audio_duration
+    dtype: float64
+  - name: speaker_id
+    dtype: string
+  - name: chapter_id
+    dtype: string
+  - name: file
+    dtype: string
+  - name: id
+    dtype: string
+  splits:
+  - name: dev
+    num_bytes: 57533473
+    num_examples: 826
+  - name: test
+    num_bytes: 59141979
+    num_examples: 871
+  - name: train
+    num_bytes: 2518553713.946
+    num_examples: 37533
+  - name: 9_hours
+    num_bytes: 141641902.42
+    num_examples: 2116
+  - name: 1_hours
+    num_bytes: 15697139
+    num_examples: 236
+  download_size: 2780836500
+  dataset_size: 2792568207.366
+- config_name: spanish
+  features:
+  - name: audio
+    dtype: audio
+  - name: original_path
+    dtype: string
+  - name: begin_time
+    dtype: float64
+  - name: end_time
+    dtype: float64
+  - name: transcript
+    dtype: string
+  - name: audio_duration
+    dtype: float64
+  - name: speaker_id
+    dtype: string
+  - name: chapter_id
+    dtype: string
+  - name: file
+    dtype: string
+  - name: id
+    dtype: string
+  splits:
+  - name: dev
+    num_bytes: 157804903.144
+    num_examples: 2408
+  - name: test
+    num_bytes: 158526899.32
+    num_examples: 2385
+  - name: train
+    num_bytes: 14562584188
+    num_examples: 220701
+  - name: 9_hours
+    num_bytes: 142473624.48
+    num_examples: 2110
+  - name: 1_hours
+    num_bytes: 15702048
+    num_examples: 233
+  download_size: 14971394533
+  dataset_size: 15037091662.944
+configs:
+- config_name: dutch
+  data_files:
+  - split: dev
+    path: dutch/dev-*
+  - split: test
+    path: dutch/test-*
+  - split: train
+    path: dutch/train-*
+  - split: 9_hours
+    path: dutch/9_hours-*
+  - split: 1_hours
+    path: dutch/1_hours-*
+- config_name: french
+  data_files:
+  - split: dev
+    path: french/dev-*
+  - split: test
+    path: french/test-*
+  - split: train
+    path: french/train-*
+  - split: 9_hours
+    path: french/9_hours-*
+  - split: 1_hours
+    path: french/1_hours-*
+- config_name: german
+  data_files:
+  - split: dev
+    path: german/dev-*
+  - split: test
+    path: german/test-*
+  - split: train
+    path: german/train-*
+  - split: 9_hours
+    path: german/9_hours-*
+  - split: 1_hours
+    path: german/1_hours-*
+- config_name: italian
+  data_files:
+  - split: dev
+    path: italian/dev-*
+  - split: test
+    path: italian/test-*
+  - split: train
+    path: italian/train-*
+  - split: 9_hours
+    path: italian/9_hours-*
+  - split: 1_hours
+    path: italian/1_hours-*
+- config_name: polish
+  data_files:
+  - split: dev
+    path: polish/dev-*
+  - split: test
+    path: polish/test-*
+  - split: train
+    path: polish/train-*
+  - split: 9_hours
+    path: polish/9_hours-*
+  - split: 1_hours
+    path: polish/1_hours-*
+- config_name: portuguese
+  data_files:
+  - split: dev
+    path: portuguese/dev-*
+  - split: test
+    path: portuguese/test-*
+  - split: train
+    path: portuguese/train-*
+  - split: 9_hours
+    path: portuguese/9_hours-*
+  - split: 1_hours
+    path: portuguese/1_hours-*
+- config_name: spanish
+  data_files:
+  - split: dev
+    path: spanish/dev-*
+  - split: test
+    path: spanish/test-*
+  - split: train
+    path: spanish/train-*
+  - split: 9_hours
+    path: spanish/9_hours-*
+  - split: 1_hours
+    path: spanish/1_hours-*
 ---
 
 # Dataset Card for MultiLingual LibriSpeech
@@ -66,11 +435,12 @@ This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
 The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream.
 
 MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 
-8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
+8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. It includes about 44.5K hours of English and a total of about 6K hours for other languages.
 
 ### Supported Tasks and Leaderboards
 
 - `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
+- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
 
 ### Languages
 
@@ -83,16 +453,13 @@ The `datasets` library allows you to load and pre-process your dataset in pure P
 For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
 ```python
 from datasets import load_dataset
-
 mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
 ```
 
 Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
 ```python
 from datasets import load_dataset
-
 mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
-
 print(next(iter(mls)))
 ```
 
@@ -103,7 +470,6 @@ Local:
 ```python
 from datasets import load_dataset
 from torch.utils.data.sampler import BatchSampler, RandomSampler
-
 mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
 batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
 dataloader = DataLoader(mls, batch_sampler=batch_sampler)
@@ -114,7 +480,6 @@ Streaming:
 ```python
 from datasets import load_dataset
 from torch.utils.data import DataLoader
-
 mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
 dataloader = DataLoader(mls, batch_size=32)
 ```
@@ -155,12 +520,11 @@ A typical data point comprises the path to the audio file, usually called `file`
 - id: unique id of the data sample.
 
 - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
-
 - chapter_id: id of the audiobook chapter which includes the transcription.
 
 ### Data Splits
 
-|                             | Train | Train.9h | Train.1h  | Dev | Test |
+|           Number of samples                  | Train | Train.9h | Train.1h  | Dev | Test |
 | -----                       | ------ | ----- | ---- | ---- | ---- | 
 | german | 469942 | 2194 | 241 | 3469 | 3394 |
 | dutch | 374287 | 2153 | 234 | 3095 | 3075 |
@@ -170,8 +534,6 @@ A typical data point comprises the path to the audio file, usually called `file`
 | portuguese | 37533 | 2116 | 236 | 826 | 871 |
 | polish | 25043 | 2173 | 238 | 512 | 520 |
 
-
-
 ## Dataset Creation
 
 ### Curation Rationale
@@ -238,7 +600,47 @@ Public Domain, Creative Commons Attribution 4.0 International Public License ([C
 }
 ```
 
+
+### Data Statistics
+
+| Duration (h) | Train     | Dev   | Test  |
+|--------------|-----------|-------|-------|
+| English      | 44,659.74 | 15.75 | 15.55 |
+| German       | 1,966.51  | 14.28 | 14.29 |
+| Dutch        | 1,554.24  | 12.76 | 12.76 |
+| French       | 1,076.58  | 10.07 | 10.07 |
+| Spanish      | 917.68    | 9.99  | 10    |
+| Italian      | 247.38    | 5.18  | 5.27  |
+| Portuguese   | 160.96    | 3.64  | 3.74  |
+| Polish       | 103.65    | 2.08  | 2.14  |
+
+| # Speakers | Train |      | Dev |    | Test |    |
+|------------|-------|------|-----|----|------|----|
+|       Gender   | M     | F    | M   | F  | M    | F  |
+| English    | 2742  | 2748 | 21  | 21 | 21   | 21 |
+| German     | 81    | 95   | 15  | 15 | 15   | 15 |
+| Dutch      | 9     | 31   | 3   | 3  | 3    | 3  |
+| French     | 62    | 80   | 9   | 9  | 9    | 9  |
+| Spanish    | 36    | 50   | 10  | 10 | 10   | 10 |
+| Italian    | 22    | 43   | 5   | 5  | 5    | 5  |
+| Portuguese | 26    | 16   | 5   | 5  | 5    | 5  |
+| Polish     | 6     | 5    | 2   | 2  | 2    | 2  |
+
+| # Hours / Gender | Dev  |      | Test |      |
+|------------------|------|------|------|------|
+|       Gender   | M    | F    | M    | F    |
+| English          | 7.76 | 7.99 | 7.62 | 7.93 |
+| German           | 7.06 | 7.22 | 7    | 7.29 |
+| Dutch            | 6.44 | 6.32 | 6.72 | 6.04 |
+| French           | 5.13 | 4.94 | 5.04 | 5.02 |
+| Spanish          | 4.91 | 5.08 | 4.78 | 5.23 |
+| Italian          | 2.5  | 2.68 | 2.38 | 2.9  |
+| Portuguese       | 1.84 | 1.81 | 1.83 | 1.9  |
+| Polish           | 1.12 | 0.95 | 1.09 | 1.05 |
+
+
+
+
 ### Contributions
 
-Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) 
-and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
+Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.

create_dataset.py

@@ -0,0 +1,106 @@
+import os
+from datasets import DatasetDict, Audio
+import pandas as pd
+from datasets.table import embed_table_storage
+import argparse
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    
+    
+    parser.add_argument("main_folder_path", type=str, help="Path of the base mls folder")
+    parser.add_argument("configuration", type=str, help="Dataset configuration to use, if necessary. Here corresponds to the language name.")
+    parser.add_argument("output_dir", type=str, help="Save the dataset on disk with this path.")
+
+    parser.add_argument("--cpu_num_workers", default=1, type=int, help="Number of CPU workers.")
+    parser.add_argument("--csv_folder_path", default=None, type=str, help="Path where to save intermediate csv, by default will be main_foldr_path")
+    parser.add_argument("--repo_id", default="facebook/multilingual_librispeech", type=str, help="Push the dataset to the hub.")
+
+
+    args = parser.parse_args()
+
+    main_folder_path = args.main_folder_path
+    csv_folder_path = args.csv_folder_path if args.csv_folder_path is not None else main_folder_path
+    if not os.path.exists(csv_folder_path):
+        os.makedirs(csv_folder_path)
+
+    splits = ["dev", "test", "train"]
+
+    # total_length_per_split = 10_000 * 60 * 60 # in sec -> 10k hours
+
+    csv_dict = {}
+    for split in splits:
+        segment_path = os.path.join(main_folder_path, split, "segments.txt")
+        transcript_path = os.path.join(main_folder_path, split, "transcripts.txt")
+
+        segments = pd.read_csv(segment_path, sep='\t', names=["audio", "original_path", "begin_time", "end_time"],
+                            index_col="audio")
+        transcripts = pd.read_csv(transcript_path, sep='\t', names=["audio", "transcript"], index_col="audio")
+
+        df = pd.concat([segments, transcripts], axis=1, join="inner")
+        print(
+            f"Segments and transcripts of {split} has been joined: new length {len(df)}, old lengths {(len(segments), len(transcripts))}")
+
+        # add audio duration
+        df["audio_duration"] = df["end_time"] - df["begin_time"]
+        df["split"] = split
+
+        print(f"len df {len(df)}")
+
+        df.to_csv(os.path.join(csv_folder_path, f"{split}.csv"))
+        csv_dict[split] = os.path.join(csv_folder_path, f"{split}.csv")
+        
+        # take care of /limited_supervision
+        if split == "train":
+            nine_hours_segment_path = os.path.join(main_folder_path, "train/limited_supervision/9hr/handles.txt")
+            nine_hours_segment = pd.read_csv(nine_hours_segment_path, sep='\t', names=["audio"], index_col="audio").index
+            nine_hours_df = df.filter(items=nine_hours_segment, axis=0)
+            nine_hours_df.to_csv(os.path.join(csv_folder_path, f"9_hours.csv"))
+            csv_dict["9_hours"] = os.path.join(csv_folder_path, f"9_hours.csv")
+            
+            one_hours_segments = [ os.path.join(f.path, "handles.txt") for f in os.scandir( os.path.join(main_folder_path, "train/limited_supervision/1hr")) if f.is_dir()]
+            one_hours_segments = pd.concat([pd.read_csv(one, sep='\t', names=["audio"], index_col="audio") for one in one_hours_segments], axis=0).index
+            one_hours_df = df.filter(items=one_hours_segments, axis=0)
+            one_hours_df.to_csv(os.path.join(csv_folder_path, f"1_hours.csv"))
+            csv_dict["1_hours"] = os.path.join(csv_folder_path, f"1_hours.csv")
+
+            
+            
+
+    dataset = DatasetDict.from_csv(csv_dict)
+
+    def extract_speaker_id_and_format_path(audio, split):
+        speaker_id = audio.split("_")[0]
+        chapter_id = audio.split("_")[1]
+        file = f"{audio}.opus"
+
+        path = os.path.join(main_folder_path, split, "audio", speaker_id, chapter_id, file)
+        return {"audio": path, "speaker_id": speaker_id, "chapter_id": chapter_id, "file": file, "id": audio}
+
+    # correct audio path
+    dataset = dataset.map(extract_speaker_id_and_format_path, input_columns=["audio", "split"], num_proc=args.cpu_num_workers, remove_columns=["split"])
+    dataset = dataset.cast_column("audio", Audio())
+
+    print(dataset)
+    print(dataset["dev"][0])
+
+    print("Embed table storage")
+
+    # load_dataset(...)
+    format = dataset["train"].format
+    dataset = dataset.with_format("arrow")
+    dataset = dataset.map(embed_table_storage, batched=True, num_proc=args.cpu_num_workers)
+    dataset = dataset.with_format(**format)
+
+    
+    dataset.save_to_disk(args.output_dir, num_proc=args.cpu_num_workers)
+
+    if args.repo_id:
+        pushed = False
+        while not pushed:
+            try:
+                dataset.push_to_hub(args.repo_id, args.configuration, revision="refs/pr/15")
+                pushed = True
+            except:
+                pass

dutch/1_hours-00000-of-00001.parquet

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+size 15398863

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+size 139332979

dutch/dev-00000-of-00001.parquet

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