I’m using whisper to transcribe audio to text. I decided to use distil -whisper to speed it up. I’ve been trying to follow the instructions on Hugging Face but keep getting an error. I’m running this code sequentially and there are no issues.
**
!pip install virtualenv
!virtualenv myenv
!myenv/bing/pip install datasets
!source myenv/bin/activate
!myenv/bin/pip install --upgrade pip
!myenv/bin/pip install --upgrade transformers accelerate datasets
**
But then I add this code and I get an error.
<code>import torchfrom transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipelinefrom datasets import load_datasetdevice = "cuda:0" if torch.cuda.is_available() else "cpu"torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32model_id = "distil-whisper/distil-large-v3"model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)model.to(device)processor = AutoProcessor.from_pretrained(model_id)pipe = pipeline("automatic-speech-recognition",model=model,tokenizer=processor.tokenizer,feature_extractor=processor.feature_extractor,max_new_tokens=128,torch_dtype=torch_dtype,device=device,)dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")sample = dataset[0]["audio"]result = pipe(sample)print(result["text"])</code><code>import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-large-v3" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) </code>import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "distil-whisper/distil-large-v3" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"])
ModuleNotFoundError Traceback (most recent call last)
in <cell line: 3>()
1 import torch
2 from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
—-> 3 from datasets import load_dataset
4
5
ModuleNotFoundError: No module named ‘datasets’
I didn’t get an error when I installed datasets so I don’t understand why I’m getting an error now.