| | 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 |
| | 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 |
| | is_bgm_separate: gr.Checkbox |
| | uvr_model_size: gr.Dropdown |
| | uvr_device: gr.Dropdown |
| | uvr_segment_size: gr.Number |
| | uvr_save_file: gr.Checkbox |
| | uvr_enable_offload: gr.Checkbox |
| | """ |
| | 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 |
| | |
| | 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: gr.Number |
| | This parameter is related to faster-whisper. Exponential length penalty constant. |
| | |
| | repetition_penalty: gr.Number |
| | This parameter is related to faster-whisper. Penalty applied to the score of previously generated tokens |
| | (set > 1 to penalize). |
| | |
| | no_repeat_ngram_size: gr.Number |
| | This parameter is related to faster-whisper. Prevent repetitions of n-grams with this size (set 0 to disable). |
| | |
| | prefix: gr.Textbox |
| | This parameter is related to faster-whisper. Optional text to provide as a prefix for the first window. |
| | |
| | suppress_blank: gr.Checkbox |
| | This parameter is related to faster-whisper. Suppress blank outputs at the beginning of the sampling. |
| | |
| | suppress_tokens: gr.Textbox |
| | 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: gr.Number |
| | This parameter is related to faster-whisper. The initial timestamp cannot be later than this. |
| | |
| | word_timestamps: gr.Checkbox |
| | 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: gr.Textbox |
| | This parameter is related to faster-whisper. If word_timestamps is True, merge these punctuation symbols |
| | with the next word. |
| | |
| | append_punctuations: gr.Textbox |
| | This parameter is related to faster-whisper. If word_timestamps is True, merge these punctuation symbols |
| | with the previous word. |
| | |
| | max_new_tokens: gr.Number |
| | 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: gr.Number |
| | This parameter is related to faster-whisper and insanely-fast-whisper. The length of audio segments in seconds. |
| | If it is not None, it will overwrite the default chunk_length of the FeatureExtractor. |
| | |
| | hallucination_silence_threshold: gr.Number |
| | 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: gr.Textbox |
| | 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: gr.Number |
| | 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: gr.Number |
| | This parameter is related to faster-whisper. Number of segments to consider for the language detection. |
| | |
| | is_separate_bgm: gr.Checkbox |
| | This parameter is related to UVR. Boolean value that determines whether to separate bgm or not. |
| | |
| | uvr_model_size: gr.Dropdown |
| | This parameter is related to UVR. UVR model size. |
| | |
| | uvr_device: gr.Dropdown |
| | This parameter is related to UVR. Device to run UVR model. |
| | |
| | uvr_segment_size: gr.Number |
| | This parameter is related to UVR. Segment size for UVR model. |
| | |
| | uvr_save_file: gr.Checkbox |
| | This parameter is related to UVR. Boolean value that determines whether to save the file or not. |
| | |
| | uvr_enable_offload: gr.Checkbox |
| | This parameter is related to UVR. Boolean value that determines whether to offload the UVR model or not |
| | after each transcription. |
| | """ |
| |
|
| | 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 = "large-v3" |
| | lang: Optional[str] = None |
| | is_translate: bool = False |
| | beam_size: int = 5 |
| | log_prob_threshold: float = -1.0 |
| | no_speech_threshold: float = 0.6 |
| | compute_type: str = "float16" |
| | best_of: int = 5 |
| | patience: float = 1.0 |
| | condition_on_previous_text: bool = True |
| | prompt_reset_on_temperature: float = 0.5 |
| | initial_prompt: Optional[str] = None |
| | temperature: float = 0.0 |
| | compression_ratio_threshold: float = 2.4 |
| | vad_filter: bool = False |
| | threshold: float = 0.5 |
| | min_speech_duration_ms: int = 250 |
| | max_speech_duration_s: float = float("inf") |
| | min_silence_duration_ms: int = 2000 |
| | speech_pad_ms: int = 400 |
| | batch_size: int = 24 |
| | is_diarize: bool = False |
| | hf_token: str = "" |
| | diarization_device: str = "cuda" |
| | length_penalty: float = 1.0 |
| | repetition_penalty: float = 1.0 |
| | no_repeat_ngram_size: int = 0 |
| | prefix: Optional[str] = None |
| | suppress_blank: bool = True |
| | suppress_tokens: Optional[str] = "[-1]" |
| | max_initial_timestamp: float = 0.0 |
| | word_timestamps: bool = False |
| | prepend_punctuations: Optional[str] = "\"'“¿([{-" |
| | append_punctuations: Optional[str] = "\"'.。,，!！?？:：”)]}、" |
| | max_new_tokens: Optional[int] = None |
| | chunk_length: Optional[int] = 30 |
| | hallucination_silence_threshold: Optional[float] = None |
| | hotwords: Optional[str] = None |
| | language_detection_threshold: Optional[float] = None |
| | language_detection_segments: int = 1 |
| | is_bgm_separate: bool = False |
| | uvr_model_size: str = "UVR-MDX-NET-Inst_HQ_4" |
| | uvr_device: str = "cuda" |
| | uvr_segment_size: int = 256 |
| | uvr_save_file: bool = False |
| | uvr_enable_offload: bool = True |
| | """ |
| | 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, |
| | "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 |
| | }, |
| | "bgm_separation": { |
| | "is_separate_bgm": self.is_bgm_separate, |
| | "model_size": self.uvr_model_size, |
| | "segment_size": self.uvr_segment_size, |
| | "save_file": self.uvr_save_file, |
| | "enable_offload": self.uvr_enable_offload |
| | }, |
| | } |
| | return data |
| |
|
| | def as_list(self) -> list: |
| | """ |
| | Converts the data class attributes into a list |
| | |
| | Returns |
| | ---------- |
| | A list of Whisper parameters |
| | """ |
| | return [getattr(self, f.name) for f in fields(self)] |
| |
|
