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Update readme, `whisper-large` -> `whisper-large-v2` (#4) 

- Update readme, `whisper-large` -> `whisper-large-v2` (6827ce791b91ce7af083878b8a153bcd768c1c74)
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Arthur Zucker 2022-12-07 12:02:43 +00:00 committed by system
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README.md
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@ -174,8 +174,8 @@ The "<|en|>" token is used to specify that the speech is in english and should b
>>> import torch
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
>>> # load dummy dataset and read soundfiles
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
@ -199,8 +199,8 @@ transcription.
>>> import torch
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
>>> # load dummy dataset and read soundfiles
>>> ds = load_dataset("common_voice", "fr", split="test", streaming=True)
@ -227,8 +227,8 @@ The "<|translate|>" is used as the first decoder input token to specify the tran
>>> import torch
>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
>>> # load dummy dataset and read soundfiles
>>> ds = load_dataset("common_voice", "fr", split="test", streaming=True)
@ -245,7 +245,7 @@ The "<|translate|>" is used as the first decoder input token to specify the tran
## Evaluation
This code snippet shows how to evaluate **openai/whisper-large** on LibriSpeech's "clean" and "other" test data.
This code snippet shows how to evaluate **openai/whisper-large-v2** on LibriSpeech's "clean" and "other" test data.
```python
>>> from datasets import load_dataset
@ -257,8 +257,8 @@ This code snippet shows how to evaluate **openai/whisper-large** on LibriSpeech'
>>> librispeech_eval = load_dataset("librispeech_asr", "clean", split="test")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large").to("cuda")
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2").to("cuda")
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
>>> def map_to_pred(batch):
>>> input_features = processor(batch["audio"]["array"], return_tensors="pt").input_features