CoolFace
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asr

hf-internal-testing /librispeech_asr_dummyaudion<1K11 likes101k downloads2y agoHugging Facejapanese-asr /whisper_transcriptions.reazon_speech_all.wer_10.0.vectorized1M<n<10M0 likes87k downloads2y agoHugging Faceopenslr /librispeech_asr Dataset Card for librispeech_asr Dataset Summary LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. Supported Tasks and Leaderboards automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic… See the full description on the dataset page: https://huggingface.co/datasets/openslr/librispeech_asr.audioautomatic-speech-recognition100K<n<1M245 likes54k downloads1y agoHugging Facejapanese-asr /whisper_transcriptions.reazon_speech_allaudio10M<n<100M16 likes46k downloads2y agoHugging Facejapanese-asr /whisper_transcriptions.mls.wer_10.0.vectorized1M<n<10M1 likes36k downloads2y agoHugging Facepatrickvonplaten /librispeech_asr_dummyLibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned. Note that in order to limit the required storage for preparing this dataset, the audio is stored in the .flac format and is not converted to a float32 array. To convert, the audio file to a float32 array, please make use of the `.map()` function as follows: ```python import soundfile as sf def map_to_array(batch): speech_array, _ = sf.read(batch["file"]) batch["speech"] = speech_array return batch dataset = dataset.map(map_to_array, remove_columns=["file"]) ```1 likes21k downloads5y agoHugging Face

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