from dataclasses import dataclass, fields import gradio as gr from typing import Optional, Dict import yaml @dataclass class WhisperParameters: 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 prompt_reset_on_temperature: gr.Slider initial_prompt: gr.Textbox temperature: gr.Slider compression_ratio_threshold: gr.Number vad_filter: gr.Checkbox threshold: gr.Slider min_speech_duration_ms: gr.Number max_speech_duration_s: gr.Number min_silence_duration_ms: gr.Number speech_pad_ms: gr.Number chunk_length_s: gr.Number batch_size: gr.Number is_diarize: gr.Checkbox hf_token: gr.Textbox diarization_device: gr.Dropdown length_penalty: gr.Number repetition_penalty: gr.Number no_repeat_ngram_size: gr.Number prefix: gr.Textbox suppress_blank: gr.Checkbox suppress_tokens: gr.Textbox max_initial_timestamp: gr.Number word_timestamps: gr.Checkbox prepend_punctuations: gr.Textbox append_punctuations: gr.Textbox max_new_tokens: gr.Number chunk_length: gr.Number hallucination_silence_threshold: gr.Number hotwords: gr.Textbox language_detection_threshold: gr.Number language_detection_segments: gr.Number """ A data class for Gradio components of the Whisper Parameters. Use "before" Gradio pre-processing. This data class is used to mitigate the key-value problem between Gradio components and function parameters. Related Gradio issue: https://github.com/gradio-app/gradio/issues/2471 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. threshold: gr.Slider This parameter is related with Silero VAD. Speech threshold. Silero VAD outputs speech probabilities for each audio chunk, probabilities ABOVE this value are considered as SPEECH. It is better to tune this parameter for each dataset separately, but "lazy" 0.5 is pretty good for most datasets. min_speech_duration_ms: gr.Number This parameter is related with Silero VAD. Final speech chunks shorter min_speech_duration_ms are thrown out. max_speech_duration_s: gr.Number This parameter is related with Silero VAD. Maximum duration of speech chunks in seconds. Chunks longer than max_speech_duration_s will be split at the timestamp of the last silence that lasts more than 100ms (if any), to prevent aggressive cutting. Otherwise, they will be split aggressively just before max_speech_duration_s. min_silence_duration_ms: gr.Number This parameter is related with Silero VAD. In the end of each speech chunk wait for min_silence_duration_ms before separating it speech_pad_ms: gr.Number This parameter is related with Silero VAD. Final speech chunks are padded by speech_pad_ms each side chunk_length_s: gr.Number This parameter is related with insanely-fast-whisper pipe. Maximum length of each chunk batch_size: gr.Number This parameter is related with insanely-fast-whisper pipe. Batch size to pass to the pipe is_diarize: gr.Checkbox This parameter is related with whisperx. Boolean value that determines whether to diarize or not. hf_token: gr.Textbox This parameter is related with whisperx. Huggingface token is needed to download diarization models. Read more about : https://huggingface.co/pyannote/speaker-diarization-3.1#requirements diarization_device: gr.Dropdown This parameter is related with whisperx. Device to run diarization model length_penalty: This parameter is related to faster-whisper. Exponential length penalty constant. repetition_penalty: This parameter is related to faster-whisper. Penalty applied to the score of previously generated tokens (set > 1 to penalize). no_repeat_ngram_size: This parameter is related to faster-whisper. Prevent repetitions of n-grams with this size (set 0 to disable). prefix: This parameter is related to faster-whisper. Optional text to provide as a prefix for the first window. suppress_blank: This parameter is related to faster-whisper. Suppress blank outputs at the beginning of the sampling. suppress_tokens: This parameter is related to faster-whisper. List of token IDs to suppress. -1 will suppress a default set of symbols as defined in the model config.json file. max_initial_timestamp: This parameter is related to faster-whisper. The initial timestamp cannot be later than this. word_timestamps: This parameter is related to faster-whisper. Extract word-level timestamps using the cross-attention pattern and dynamic time warping, and include the timestamps for each word in each segment. prepend_punctuations: This parameter is related to faster-whisper. If word_timestamps is True, merge these punctuation symbols with the next word. append_punctuations: This parameter is related to faster-whisper. If word_timestamps is True, merge these punctuation symbols with the previous word. max_new_tokens: This parameter is related to faster-whisper. Maximum number of new tokens to generate per-chunk. If not set, the maximum will be set by the default max_length. chunk_length: This parameter is related to faster-whisper. The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor. hallucination_silence_threshold: This parameter is related to faster-whisper. When word_timestamps is True, skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected. hotwords: This parameter is related to faster-whisper. Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None. language_detection_threshold: This parameter is related to faster-whisper. If the maximum probability of the language tokens is higher than this value, the language is detected. language_detection_segments: This parameter is related to faster-whisper. Number of segments to consider for the language detection. """ def as_list(self) -> list: """ Converts the data class attributes into a list, Use in Gradio UI 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)] @staticmethod def as_value(*args) -> 'WhisperValues': """ To use Whisper parameters in function after Gradio post-processing. See more about Gradio post-processing: : https://www.gradio.app/docs/components Returns ---------- WhisperValues Data class that has values of parameters """ return WhisperValues(*args) @dataclass 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 prompt_reset_on_temperature: float initial_prompt: Optional[str] temperature: float compression_ratio_threshold: float vad_filter: bool threshold: float min_speech_duration_ms: int max_speech_duration_s: float min_silence_duration_ms: int speech_pad_ms: int chunk_length_s: int batch_size: int is_diarize: bool hf_token: str diarization_device: str length_penalty: float repetition_penalty: float no_repeat_ngram_size: int prefix: Optional[str] suppress_blank: bool suppress_tokens: Optional[str] max_initial_timestamp: float word_timestamps: bool prepend_punctuations: Optional[str] append_punctuations: Optional[str] max_new_tokens: Optional[int] chunk_length: Optional[int] hallucination_silence_threshold: Optional[float] hotwords: Optional[str] language_detection_threshold: Optional[float] language_detection_segments: int """ A data class to use Whisper parameters. """ def to_yaml(self) -> Dict: data = { "whisper": { "model_size": self.model_size, "lang": "Automatic Detection" if self.lang is None else self.lang, "is_translate": self.is_translate, "beam_size": self.beam_size, "log_prob_threshold": self.log_prob_threshold, "no_speech_threshold": self.no_speech_threshold, "best_of": self.best_of, "patience": self.patience, "condition_on_previous_text": self.condition_on_previous_text, "prompt_reset_on_temperature": self.prompt_reset_on_temperature, "initial_prompt": None if not self.initial_prompt else self.initial_prompt, "temperature": self.temperature, "compression_ratio_threshold": self.compression_ratio_threshold, "chunk_length_s": None if self.chunk_length_s is None else self.chunk_length_s, "batch_size": self.batch_size, "length_penalty": self.length_penalty, "repetition_penalty": self.repetition_penalty, "no_repeat_ngram_size": self.no_repeat_ngram_size, "prefix": None if not self.prefix else self.prefix, "suppress_blank": self.suppress_blank, "suppress_tokens": self.suppress_tokens, "max_initial_timestamp": self.max_initial_timestamp, "word_timestamps": self.word_timestamps, "prepend_punctuations": self.prepend_punctuations, "append_punctuations": self.append_punctuations, "max_new_tokens": self.max_new_tokens, "chunk_length": self.chunk_length, "hallucination_silence_threshold": self.hallucination_silence_threshold, "hotwords": None if not self.hotwords else self.hotwords, "language_detection_threshold": self.language_detection_threshold, "language_detection_segments": self.language_detection_segments, }, "vad": { "vad_filter": self.vad_filter, "threshold": self.threshold, "min_speech_duration_ms": self.min_speech_duration_ms, "max_speech_duration_s": self.max_speech_duration_s, "min_silence_duration_ms": self.min_silence_duration_ms, "speech_pad_ms": self.speech_pad_ms, }, "diarization": { "is_diarize": self.is_diarize, "hf_token": self.hf_token } } return data