f04f061f8c
Co-authored-by: root <root@idc708073.jf.intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import base64
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import json
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import requests
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import torch
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from datasets import load_dataset
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from evaluate import load
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from pydub import AudioSegment
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from transformers import WhisperForConditionalGeneration, WhisperProcessor
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MODEL_NAME = "openai/whisper-large-v2"
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processor = WhisperProcessor.from_pretrained(MODEL_NAME)
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librispeech_test_clean = load_dataset(
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"andreagasparini/librispeech_test_only", "clean", split="test", trust_remote_code=True
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)
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def map_to_pred(batch):
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batch["reference"] = processor.tokenizer._normalize(batch["text"])
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file_path = batch["file"]
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# process the file_path
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pidx = file_path.rfind("/")
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sidx = file_path.rfind(".")
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file_path_prefix = file_path[: pidx + 1]
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file_path_suffix = file_path[sidx:]
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file_path_mid = file_path[pidx + 1 : sidx]
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splits = file_path_mid.split("-")
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file_path_mid = f"LibriSpeech/test-clean/{splits[0]}/{splits[1]}/{file_path_mid}"
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file_path = file_path_prefix + file_path_mid + file_path_suffix
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audio = AudioSegment.from_file(file_path)
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audio.export("tmp.wav")
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with open("tmp.wav", "rb") as f:
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test_audio_base64_str = base64.b64encode(f.read()).decode("utf-8")
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inputs = {"audio": test_audio_base64_str}
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endpoint = "http://localhost:7066/v1/asr"
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response = requests.post(url=endpoint, data=json.dumps(inputs), proxies={"http": None})
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result_str = response.json()["asr_result"]
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batch["prediction"] = processor.tokenizer._normalize(result_str)
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return batch
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result = librispeech_test_clean.map(map_to_pred)
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wer = load("wer")
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print(100 * wer.compute(references=result["reference"], predictions=result["prediction"]))
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