wehub-resource-sync
82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
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""" This example shows how to use llmware provided sample files for testing with WhisperCPP, integrated as of
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llmware 0.2.11.
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# examples - "famous_quotes" | "greatest_speeches" | "youtube_demos" | "earnings_calls"
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-- famous_quotes - approximately 20 small .wav files with clips from old movies and speeches
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-- greatest_speeches - approximately 60 famous historical speeches in english
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-- youtube_videos - wav files of ~3 llmware youtube videos
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-- earnings_calls - wav files of ~4 public company earnings calls (gathered from public investor relations)
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These sample files are hosted in a non-restricted AWS S3 bucket, and downloaded via the Setup method
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`load_sample_voice_files`. There are two options:
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-- small_only = True: only pulls the 'famous_quotes' samples
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-- small_only = False: pulls all of the samples (requires ~1.9 GB in total)
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Please note that all of these samples have been pulled from open public domain sources, including the
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Internet Archives, e.g., https://archive.org. These sample files are being provided solely for the purpose of
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testing the code scripts below. Please do not use them for any other purpose.
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To run these examples, please make sure to `pip install librosa`
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"""
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import os
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from llmware.models import ModelCatalog
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from llmware.gguf_configs import GGUFConfigs
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from llmware.setup import Setup
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# optional / to adjust various parameters of the model
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GGUFConfigs().set_config("whisper_cpp_verbose", "OFF")
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GGUFConfigs().set_config("whisper_cpp_realtime_display", True)
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# note: english is default output - change to 'es' | 'fr' | 'de' | 'it' ...
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GGUFConfigs().set_config("whisper_language", "en")
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GGUFConfigs().set_config("whisper_remove_segment_markers", True)
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def sample_files(example="famous_quotes", small_only=False):
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""" Execute a basic inference on Voice-to-Text model passing a file_path string """
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voice_samples = Setup().load_voice_sample_files(small_only=small_only)
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examples = ["famous_quotes", "greatest_speeches", "youtube_demos", "earnings_calls"]
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if example not in examples:
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print("choose one of the following - ", examples)
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return 0
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fp = os.path.join(voice_samples,example)
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files = os.listdir(fp)
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# these are the two key lines
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whisper_base_english = "whisper-cpp-base-english"
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model = ModelCatalog().load_model(whisper_base_english)
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for f in files:
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if f.endswith(".wav"):
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prompt = os.path.join(fp,f)
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print(f"\n\nPROCESSING: prompt = {prompt}")
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response = model.inference(prompt)
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print("\nllm response: ", response["llm_response"])
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print("usage: ", response["usage"])
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return 0
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if __name__ == "__main__":
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# pick among the four examples: famous_quotes | greatest_speeches | youtube_demos | earnings_calls
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sample_files(example="famous_quotes", small_only=False)
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