Update README.md
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README.md
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@@ -85,13 +85,15 @@ Speculative Decoding was proposed in Fast Inference from Transformers via Specul
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```bash
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import torch
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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dataset = load_dataset("bond005/sberdevices_golos_10h_crowd", split="validation", token=True)
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model_id = "Val123val/ru_whisper_small"
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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processor = AutoProcessor.from_pretrained(model_id)
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assistant_model_id = "openai/whisper-tiny"
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assistant_model = AutoModelForSpeechSeq2Seq.from_pretrained(
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assistant_model.to(device);
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from transformers import pipeline
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pipe = pipeline(
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"automatic-speech-recognition",
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model=model,
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```bash
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import torch
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
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from transformers import pipeline
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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# load dataset
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dataset = load_dataset("bond005/sberdevices_golos_10h_crowd", split="validation", token=True)
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# load model
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model_id = "Val123val/ru_whisper_small"
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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processor = AutoProcessor.from_pretrained(model_id)
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# load assistant model
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assistant_model_id = "openai/whisper-tiny"
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assistant_model = AutoModelForSpeechSeq2Seq.from_pretrained(
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assistant_model.to(device);
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# make pipe
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pipe = pipeline(
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"automatic-speech-recognition",
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model=model,
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