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| from dataclasses import dataclass, fields | |
| import gradio as gr | |
| from typing import Optional | |
| class WhisperGradioComponents: | |
| model_size: gr.Dropdown | |
| lang: gr.Dropdown | |
| is_translate: gr.Checkbox | |
| beam_size: gr.Number | |
| log_prob_threshold: gr.Number | |
| no_speech_threshold: gr.Number | |
| compute_type: gr.Dropdown | |
| best_of: gr.Number | |
| patience: gr.Number | |
| condition_on_previous_text: gr.Checkbox | |
| initial_prompt: gr.Textbox | |
| temperature: gr.Slider | |
| compression_ratio_threshold: gr.Number | |
| vad_filter: gr.Checkbox | |
| """ | |
| A data class for Gradio components of the Whisper Parameters. Use "before" Gradio pre-processing. | |
| See more about Gradio pre-processing: https://www.gradio.app/docs/components | |
| Attributes | |
| ---------- | |
| model_size: gr.Dropdown | |
| Whisper model size. | |
| lang: gr.Dropdown | |
| Source language of the file to transcribe. | |
| is_translate: gr.Checkbox | |
| Boolean value that determines whether to translate to English. | |
| It's Whisper's feature to translate speech from another language directly into English end-to-end. | |
| beam_size: gr.Number | |
| Int value that is used for decoding option. | |
| log_prob_threshold: gr.Number | |
| If the average log probability over sampled tokens is below this value, treat as failed. | |
| no_speech_threshold: gr.Number | |
| If the no_speech probability is higher than this value AND | |
| the average log probability over sampled tokens is below `log_prob_threshold`, | |
| consider the segment as silent. | |
| compute_type: gr.Dropdown | |
| compute type for transcription. | |
| see more info : https://opennmt.net/CTranslate2/quantization.html | |
| best_of: gr.Number | |
| Number of candidates when sampling with non-zero temperature. | |
| patience: gr.Number | |
| Beam search patience factor. | |
| condition_on_previous_text: gr.Checkbox | |
| if True, the previous output of the model is provided as a prompt for the next window; | |
| disabling may make the text inconsistent across windows, but the model becomes less prone to | |
| getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. | |
| initial_prompt: gr.Textbox | |
| Optional text to provide as a prompt for the first window. This can be used to provide, or | |
| "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns | |
| to make it more likely to predict those word correctly. | |
| temperature: gr.Slider | |
| Temperature for sampling. It can be a tuple of temperatures, | |
| which will be successively used upon failures according to either | |
| `compression_ratio_threshold` or `log_prob_threshold`. | |
| compression_ratio_threshold: gr.Number | |
| If the gzip compression ratio is above this value, treat as failed | |
| vad_filter: gr.Checkbox | |
| Enable the voice activity detection (VAD) to filter out parts of the audio | |
| without speech. This step is using the Silero VAD model | |
| https://github.com/snakers4/silero-vad. | |
| """ | |
| def to_list(self) -> list: | |
| """ | |
| Converts the data class attributes into a list. Use "before" Gradio pre-processing. | |
| See more about Gradio pre-processing: : https://www.gradio.app/docs/components | |
| Returns | |
| ---------- | |
| A list of Gradio components | |
| """ | |
| return [getattr(self, f.name) for f in fields(self)] | |
| class WhisperValues: | |
| model_size: str | |
| lang: str | |
| is_translate: bool | |
| beam_size: int | |
| log_prob_threshold: float | |
| no_speech_threshold: float | |
| compute_type: str | |
| best_of: int | |
| patience: float | |
| condition_on_previous_text: bool | |
| initial_prompt: Optional[str] | |
| temperature: float | |
| compression_ratio_threshold: float | |
| vad_filter: bool | |
| """ | |
| A data class to use Whisper parameters. Use "after" Gradio pre-processing. | |
| See more about Gradio pre-processing: : https://www.gradio.app/docs/components | |
| """ | |