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.run_speech_recognition_seq2seq_streaming.py.swp
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version https://git-lfs.github.com/spec/v1
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oid sha256:5afad038ca66e4a3fe2adc83255d2b1830295ec385663ae7117e72f55f25982a
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size 20480
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- fleurs
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metrics:
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- wer
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model-index:
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- name:
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: fleurs
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type: fleurs
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config: ps_af
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split: test
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args: ps_af
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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#
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fleurs dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2309
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- Wer: 56.6510
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---
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license: apache-2.0
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tags:
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- whisper-event
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- generated_from_trainer
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datasets:
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- google/fleurs
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metrics:
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- wer
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model-index:
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- name: Whisper Small Pashto
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: google/fleurs ps_af
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type: google/fleurs
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config: ps_af
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split: test
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args: ps_af
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Whisper Small Pashto
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs ps_af dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.2309
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- Wer: 56.6510
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run_speech_recognition_seq2seq_streaming.py
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elif isinstance(train_dataloader.dataset, IterableDataset):
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train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
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model.config.dropout=0.
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# Initialize Trainer
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trainer = Seq2SeqTrainer(
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elif isinstance(train_dataloader.dataset, IterableDataset):
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train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1)
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model.config.dropout=0.1
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# Initialize Trainer
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trainer = Seq2SeqTrainer(
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