38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
from functools import reduce
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from helper.token_counter import count_tokens
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# model = whisper.load_model("base")
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# file_path = "audio-samples/mynameisjeff.wav"
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# audio_file = AudioSegment.from_file(file_path)
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# if os.path.exists(file_path):
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# result = model.transcribe(file_path, fp16=False, language='English', verbose=True)
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# print(result["text"])
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# else:
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# print("File not found:", file_path)
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messages = [
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{
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"role": "system",
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"content": "You are a IELTS examiner.",
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},
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{
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"role": "system",
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"content": f"The question you have to grade is of type and is the following: ",
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},
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{
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"role": "system",
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"content": "Please provide a JSON object response with the overall grade and breakdown grades, "
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"formatted as follows: {'overall': 7.0, 'task_response': {'Task Achievement': 8.0, "
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"'Coherence and Cohesion': 6.5, 'Lexical Resource': 7.5, 'Grammatical Range and Accuracy': "
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"6.0}}",
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},
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{
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"role": "user",
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"content": f"Evaluate this answer according to ielts grading system:",
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},
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]
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token_count = reduce(lambda count, item: count + count_tokens(item)['n_tokens'],
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map(lambda x: x["content"], filter(lambda x: "content" in x, messages)), 0)
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print(token_count)
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