Speech-to-Text
Collection
collection of OpenVINO optimized models for automatic speech recognition
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18 items
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Updated
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2
The provided OpenVINO™ IR model is compatible with:
pip install optimum[openvino]
from transformers import AutoProcessor
from optimum.intel.openvino import OVModelForSpeechSeq2Seq
model_id = "OpenVINO/distil-large-v2-fp16-ov"
tokenizer = AutoProcessor.from_pretrained(model_id)
model = OVModelForSpeechSeq2Seq.from_pretrained(model_id)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
sample = dataset[0]
input_features = processor(
sample["audio"]["array"],
sampling_rate=sample["audio"]["sampling_rate"],
return_tensors="pt",
).input_features
outputs = model.generate(input_features)
text = processor.batch_decode(outputs)[0]
print(text)
pip install huggingface_hub
pip install -U --pre --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly openvino openvino-tokenizers openvino-genai
import huggingface_hub as hf_hub
model_id = "OpenVINO/distil-large-v2-fp16-ov"
model_path = "distil-large-v2-fp16-ov"
hf_hub.snapshot_download(model_id, local_dir=model_path)
import openvino_genai as ov_genai
import datasets
device = "CPU"
pipe = ov_genai.WhisperPipeline(model_path, device)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
sample = dataset[0]["audio]["array"]
print(pipe.generate(sample))
More GenAI usage examples can be found in OpenVINO GenAI library docs and samples
Check the original model card for original model card for limitations.
The original model is distributed under mit license. More details can be found in original model card.
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Base model
distil-whisper/distil-large-v2