Delete modules/whisper/whisper_parameter.py

modules/whisper/whisper_parameter.py

@@ -1,381 +0,0 @@
-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
-    whisper_enable_offload: gr.Checkbox
-    diarization_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.
-
-    whisper_enable_offload: gr.Checkbox
-        This parameter is related to Whisper. Boolean value that determines whether to offload the Whisper model.
-
-    diarization_enable_offload: gr.Checkbox
-        This parameter is related to diarization. Boolean value that determines whether to offload the diarization model.
-    """
-
-    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-v2"
-    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
-    whisper_enable_offload: bool = True
-    diarization_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,
-                "enable_offload": self.whisper_enable_offload
-            },
-            "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,
-                "enable_offload": self.diarization_enable_offload
-            },
-            "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)]