import os import argparse import gradio as gr from modules.whisper.whisper_Inference import WhisperInference from modules.whisper.faster_whisper_inference import FasterWhisperInference from modules.whisper.insanely_fast_whisper_inference import InsanelyFastWhisperInference from modules.translation.nllb_inference import NLLBInference from ui.htmls import * from modules.utils.youtube_manager import get_ytmetas from modules.translation.deepl_api import DeepLAPI from modules.whisper.whisper_parameter import * class App: def __init__(self, args): self.args = args self.app = gr.Blocks(css=CSS, theme=self.args.theme) self.whisper_inf = self.init_whisper() print(f"Use \"{self.args.whisper_type}\" implementation") print(f"Device \"{self.whisper_inf.device}\" is detected") self.nllb_inf = NLLBInference( model_dir=self.args.nllb_model_dir, output_dir=os.path.join(self.args.output_dir, "translations") ) self.deepl_api = DeepLAPI( output_dir=self.args.output_dir ) def init_whisper(self): # Temporal fix of the issue : https://github.com/jhj0517/Whisper-WebUI/issues/144 os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' whisper_type = self.args.whisper_type.lower().strip() if whisper_type in ["faster_whisper", "faster-whisper", "fasterwhisper"]: whisper_inf = FasterWhisperInference( model_dir=self.args.faster_whisper_model_dir, output_dir=self.args.output_dir, args=self.args ) elif whisper_type in ["whisper"]: whisper_inf = WhisperInference( model_dir=self.args.whisper_model_dir, output_dir=self.args.output_dir, args=self.args ) elif whisper_type in ["insanely_fast_whisper", "insanely-fast-whisper", "insanelyfastwhisper", "insanely_faster_whisper", "insanely-faster-whisper", "insanelyfasterwhisper"]: whisper_inf = InsanelyFastWhisperInference( model_dir=self.args.insanely_fast_whisper_model_dir, output_dir=self.args.output_dir, args=self.args ) else: whisper_inf = FasterWhisperInference( model_dir=self.args.faster_whisper_model_dir, output_dir=self.args.output_dir, args=self.args ) return whisper_inf @staticmethod def open_folder(folder_path: str): if os.path.exists(folder_path): os.system(f"start {folder_path}") else: print(f"The folder {folder_path} does not exist.") @staticmethod def on_change_models(model_size: str): translatable_model = ["large", "large-v1", "large-v2", "large-v3"] if model_size not in translatable_model: return gr.Checkbox(visible=False, value=False, interactive=False) else: return gr.Checkbox(visible=True, value=False, label="Translate to English?", interactive=True) def launch(self): with self.app: with gr.Row(): with gr.Column(): gr.Markdown(MARKDOWN, elem_id="md_project") with gr.Tabs(): with gr.TabItem("File"): # tab1 with gr.Column(): input_file = gr.Files(type="filepath", label="Upload File here") tb_input_folder = gr.Textbox(label="Input Folder Path (Optional)", info="Optional: Specify the folder path where the input files are located, if you prefer to use local files instead of uploading them." " Leave this field empty if you do not wish to use a local path.", visible=self.args.colab, value="") with gr.Row(): dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-v2", label="Model") dd_lang = gr.Dropdown(choices=["Automatic Detection"] + self.whisper_inf.available_langs, value="Automatic Detection", label="Language") dd_file_format = gr.Dropdown(["SRT", "WebVTT", "txt"], value="SRT", label="File Format") with gr.Row(): cb_translate = gr.Checkbox(value=False, label="Translate to English?", interactive=True) with gr.Row(): cb_timestamp = gr.Checkbox(value=True, label="Add a timestamp to the end of the filename", interactive=True) with gr.Accordion("Advanced Parameters", open=False): nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True) nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True) nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True) dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True) nb_best_of = gr.Number(label="Best Of", value=5, interactive=True) nb_patience = gr.Number(label="Patience", value=1, interactive=True) cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True) tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True) sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True) nb_compression_ratio_threshold = gr.Number(label="Compression Ratio Threshold", value=2.4, interactive=True) with gr.Group(visible=isinstance(self.whisper_inf, FasterWhisperInference)): with gr.Column(): nb_length_penalty = gr.Number(label="Length Penalty", value=1, info="Exponential length penalty constant.") nb_repetition_penalty = gr.Number(label="Repetition Penalty", value=1, info="Penalty applied to the score of previously generated tokens (set > 1 to penalize).") nb_no_repeat_ngram_size = gr.Number(label="No Repeat N-gram Size", value=0, precision=0, info="Prevent repetitions of n-grams with this size (set 0 to disable).") tb_prefix = gr.Textbox(label="Prefix", value=None, info="Optional text to provide as a prefix for the first window.") cb_suppress_blank = gr.Checkbox(label="Suppress Blank", value=True, info="Suppress blank outputs at the beginning of the sampling.") tb_suppress_tokens = gr.Textbox(label="Suppress Tokens", value="-1", info="List of token IDs to suppress. -1 will suppress a default set of symbols as defined in the model config.json file.") nb_max_initial_timestamp = gr.Number(label="Max Initial Timestamp", value=1.0, info="The initial timestamp cannot be later than this.") cb_word_timestamps = gr.Checkbox(label="Word Timestamps", value=False, info="Extract word-level timestamps using the cross-attention pattern and dynamic time warping, and include the timestamps for each word in each segment.") tb_prepend_punctuations = gr.Textbox(label="Prepend Punctuations", value="\"'“¿([{-", info="If word_timestamps is True, merge these punctuation symbols with the next word.") tb_append_punctuations = gr.Textbox(label="Append Punctuations", value="\"'.。,，!！?？:：”)]}、", info="If word_timestamps is True, merge these punctuation symbols with the previous word.") nb_max_new_tokens = gr.Number(label="Max New Tokens", value=None, precision=0, info="Maximum number of new tokens to generate per-chunk. If not set, the maximum will be set by the default max_length.") nb_chunk_length = gr.Number(label="Chunk Length", value=None, info="The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor.") nb_hallucination_silence_threshold = gr.Number(label="Hallucination Silence Threshold", value=None, info="When word_timestamps is True, skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected.") tb_hotwords = gr.Textbox(label="Hotwords", value=None, info="Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None.") nb_language_detection_threshold = gr.Number(label="Language Detection Threshold", value=None, info="If the maximum probability of the language tokens is higher than this value, the language is detected.") nb_language_detection_segments = gr.Number(label="Language Detection Segments", value=1, precision=0, info="Number of segments to consider for the language detection.") with gr.Group(visible=isinstance(self.whisper_inf, InsanelyFastWhisperInference)): nb_chunk_length_s = gr.Number(label="Chunk Lengths (sec)", value=30, precision=0) nb_batch_size = gr.Number(label="Batch Size", value=24, precision=0) with gr.Accordion("VAD", open=False): cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True) sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5, info="Lower it to be more sensitive to small sounds.") nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250) nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999) nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000) nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400) with gr.Accordion("Diarization", open=False): cb_diarize = gr.Checkbox(label="Enable Diarization") tb_hf_token = gr.Text(label="HuggingFace Token", value="", info="This is only needed the first time you download the model. If you already have models, you don't need to enter. " "To download the model, you must manually go to \"https://huggingface.co/pyannote/speaker-diarization-3.1\" and agree to their requirement.") dd_diarization_device = gr.Dropdown(label="Device", choices=self.whisper_inf.diarizer.get_available_device(), value=self.whisper_inf.diarizer.get_device()) with gr.Row(): btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary") with gr.Row(): tb_indicator = gr.Textbox(label="Output", scale=5) files_subtitles = gr.Files(label="Downloadable output file", scale=3, interactive=False) btn_openfolder = gr.Button('📂', scale=1) params = [input_file, tb_input_folder, dd_file_format, cb_timestamp] whisper_params = WhisperParameters( model_size=dd_model, lang=dd_lang, is_translate=cb_translate, beam_size=nb_beam_size, log_prob_threshold=nb_log_prob_threshold, no_speech_threshold=nb_no_speech_threshold, compute_type=dd_compute_type, best_of=nb_best_of, patience=nb_patience, condition_on_previous_text=cb_condition_on_previous_text, initial_prompt=tb_initial_prompt, temperature=sd_temperature, compression_ratio_threshold=nb_compression_ratio_threshold, vad_filter=cb_vad_filter, threshold=sd_threshold, min_speech_duration_ms=nb_min_speech_duration_ms, max_speech_duration_s=nb_max_speech_duration_s, min_silence_duration_ms=nb_min_silence_duration_ms, speech_pad_ms=nb_speech_pad_ms, chunk_length_s=nb_chunk_length_s, batch_size=nb_batch_size, is_diarize=cb_diarize, hf_token=tb_hf_token, diarization_device=dd_diarization_device, length_penalty=nb_length_penalty, repetition_penalty=nb_repetition_penalty, no_repeat_ngram_size=nb_no_repeat_ngram_size, prefix=tb_prefix, suppress_blank=cb_suppress_blank, suppress_tokens=tb_suppress_tokens, max_initial_timestamp=nb_max_initial_timestamp, word_timestamps=cb_word_timestamps, prepend_punctuations=tb_prepend_punctuations, append_punctuations=tb_append_punctuations, max_new_tokens=nb_max_new_tokens, chunk_length=nb_chunk_length, hallucination_silence_threshold=nb_hallucination_silence_threshold, hotwords=tb_hotwords, language_detection_threshold=nb_language_detection_threshold, language_detection_segments=nb_language_detection_segments ) btn_run.click(fn=self.whisper_inf.transcribe_file, inputs=params + whisper_params.as_list(), outputs=[tb_indicator, files_subtitles]) btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None) dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate]) with gr.TabItem("Youtube"): # tab2 with gr.Row(): tb_youtubelink = gr.Textbox(label="Youtube Link") with gr.Row(equal_height=True): with gr.Column(): img_thumbnail = gr.Image(label="Youtube Thumbnail") with gr.Column(): tb_title = gr.Label(label="Youtube Title") tb_description = gr.Textbox(label="Youtube Description", max_lines=15) with gr.Row(): dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-v2", label="Model") dd_lang = gr.Dropdown(choices=["Automatic Detection"] + self.whisper_inf.available_langs, value="Automatic Detection", label="Language") dd_file_format = gr.Dropdown(choices=["SRT", "WebVTT", "txt"], value="SRT", label="File Format") with gr.Row(): cb_translate = gr.Checkbox(value=False, label="Translate to English?", interactive=True) with gr.Row(): cb_timestamp = gr.Checkbox(value=True, label="Add a timestamp to the end of the filename", interactive=True) with gr.Accordion("Advanced Parameters", open=False): nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True) nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True) nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True) dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True) nb_best_of = gr.Number(label="Best Of", value=5, interactive=True) nb_patience = gr.Number(label="Patience", value=1, interactive=True) cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True) tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True) sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True) nb_compression_ratio_threshold = gr.Number(label="Compression Ratio Threshold", value=2.4, interactive=True) with gr.Group(visible=isinstance(self.whisper_inf, FasterWhisperInference)): with gr.Column(): nb_length_penalty = gr.Number(label="Length Penalty", value=1, info="Exponential length penalty constant.") nb_repetition_penalty = gr.Number(label="Repetition Penalty", value=1, info="Penalty applied to the score of previously generated tokens (set > 1 to penalize).") nb_no_repeat_ngram_size = gr.Number(label="No Repeat N-gram Size", value=0, precision=0, info="Prevent repetitions of n-grams with this size (set 0 to disable).") tb_prefix = gr.Textbox(label="Prefix", value=None, info="Optional text to provide as a prefix for the first window.") cb_suppress_blank = gr.Checkbox(label="Suppress Blank", value=True, info="Suppress blank outputs at the beginning of the sampling.") tb_suppress_tokens = gr.Textbox(label="Suppress Tokens", value="-1", info="List of token IDs to suppress. -1 will suppress a default set of symbols as defined in the model config.json file.") nb_max_initial_timestamp = gr.Number(label="Max Initial Timestamp", value=1.0, info="The initial timestamp cannot be later than this.") cb_word_timestamps = gr.Checkbox(label="Word Timestamps", value=False, info="Extract word-level timestamps using the cross-attention pattern and dynamic time warping, and include the timestamps for each word in each segment.") tb_prepend_punctuations = gr.Textbox(label="Prepend Punctuations", value="\"'“¿([{-", info="If word_timestamps is True, merge these punctuation symbols with the next word.") tb_append_punctuations = gr.Textbox(label="Append Punctuations", value="\"'.。,，!！?？:：”)]}、", info="If word_timestamps is True, merge these punctuation symbols with the previous word.") nb_max_new_tokens = gr.Number(label="Max New Tokens", value=None, precision=0, info="Maximum number of new tokens to generate per-chunk. If not set, the maximum will be set by the default max_length.") nb_chunk_length = gr.Number(label="Chunk Length", value=None, info="The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor.") nb_hallucination_silence_threshold = gr.Number(label="Hallucination Silence Threshold", value=None, info="When word_timestamps is True, skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected.") tb_hotwords = gr.Textbox(label="Hotwords", value=None, info="Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None.") nb_language_detection_threshold = gr.Number(label="Language Detection Threshold", value=None, info="If the maximum probability of the language tokens is higher than this value, the language is detected.") nb_language_detection_segments = gr.Number(label="Language Detection Segments", value=1, precision=0, info="Number of segments to consider for the language detection.") with gr.Group(visible=isinstance(self.whisper_inf, InsanelyFastWhisperInference)): nb_chunk_length_s = gr.Number(label="Chunk Lengths (sec)", value=30, precision=0) nb_batch_size = gr.Number(label="Batch Size", value=24, precision=0) with gr.Accordion("VAD", open=False): cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True) sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5, info="Lower it to be more sensitive to small sounds.") nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250) nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999) nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000) nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400) with gr.Accordion("Diarization", open=False): cb_diarize = gr.Checkbox(label="Enable Diarization") tb_hf_token = gr.Text(label="HuggingFace Token", value="", info="This is only needed the first time you download the model. If you already have models, you don't need to enter. " "To download the model, you must manually go to \"https://huggingface.co/pyannote/speaker-diarization-3.1\" and agree to their requirement.") dd_diarization_device = gr.Dropdown(label="Device", choices=self.whisper_inf.diarizer.get_available_device(), value=self.whisper_inf.diarizer.get_device()) with gr.Row(): btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary") with gr.Row(): tb_indicator = gr.Textbox(label="Output", scale=5) files_subtitles = gr.Files(label="Downloadable output file", scale=3) btn_openfolder = gr.Button('📂', scale=1) params = [tb_youtubelink, dd_file_format, cb_timestamp] whisper_params = WhisperParameters( model_size=dd_model, lang=dd_lang, is_translate=cb_translate, beam_size=nb_beam_size, log_prob_threshold=nb_log_prob_threshold, no_speech_threshold=nb_no_speech_threshold, compute_type=dd_compute_type, best_of=nb_best_of, patience=nb_patience, condition_on_previous_text=cb_condition_on_previous_text, initial_prompt=tb_initial_prompt, temperature=sd_temperature, compression_ratio_threshold=nb_compression_ratio_threshold, vad_filter=cb_vad_filter, threshold=sd_threshold, min_speech_duration_ms=nb_min_speech_duration_ms, max_speech_duration_s=nb_max_speech_duration_s, min_silence_duration_ms=nb_min_silence_duration_ms, speech_pad_ms=nb_speech_pad_ms, chunk_length_s=nb_chunk_length_s, batch_size=nb_batch_size, is_diarize=cb_diarize, hf_token=tb_hf_token, diarization_device=dd_diarization_device, length_penalty=nb_length_penalty, repetition_penalty=nb_repetition_penalty, no_repeat_ngram_size=nb_no_repeat_ngram_size, prefix=tb_prefix, suppress_blank=cb_suppress_blank, suppress_tokens=tb_suppress_tokens, max_initial_timestamp=nb_max_initial_timestamp, word_timestamps=cb_word_timestamps, prepend_punctuations=tb_prepend_punctuations, append_punctuations=tb_append_punctuations, max_new_tokens=nb_max_new_tokens, chunk_length=nb_chunk_length, hallucination_silence_threshold=nb_hallucination_silence_threshold, hotwords=tb_hotwords, language_detection_threshold=nb_language_detection_threshold, language_detection_segments=nb_language_detection_segments ) btn_run.click(fn=self.whisper_inf.transcribe_youtube, inputs=params + whisper_params.as_list(), outputs=[tb_indicator, files_subtitles]) tb_youtubelink.change(get_ytmetas, inputs=[tb_youtubelink], outputs=[img_thumbnail, tb_title, tb_description]) btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None) dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate]) with gr.TabItem("Mic"): # tab3 with gr.Row(): mic_input = gr.Microphone(label="Record with Mic", type="filepath", interactive=True) with gr.Row(): dd_model = gr.Dropdown(choices=self.whisper_inf.available_models, value="large-v2", label="Model") dd_lang = gr.Dropdown(choices=["Automatic Detection"] + self.whisper_inf.available_langs, value="Automatic Detection", label="Language") dd_file_format = gr.Dropdown(["SRT", "WebVTT", "txt"], value="SRT", label="File Format") with gr.Row(): cb_translate = gr.Checkbox(value=False, label="Translate to English?", interactive=True) with gr.Accordion("Advanced Parameters", open=False): nb_beam_size = gr.Number(label="Beam Size", value=1, precision=0, interactive=True) nb_log_prob_threshold = gr.Number(label="Log Probability Threshold", value=-1.0, interactive=True) nb_no_speech_threshold = gr.Number(label="No Speech Threshold", value=0.6, interactive=True) dd_compute_type = gr.Dropdown(label="Compute Type", choices=self.whisper_inf.available_compute_types, value=self.whisper_inf.current_compute_type, interactive=True) nb_best_of = gr.Number(label="Best Of", value=5, interactive=True) nb_patience = gr.Number(label="Patience", value=1, interactive=True) cb_condition_on_previous_text = gr.Checkbox(label="Condition On Previous Text", value=True, interactive=True) tb_initial_prompt = gr.Textbox(label="Initial Prompt", value=None, interactive=True) sd_temperature = gr.Slider(label="Temperature", value=0, step=0.01, maximum=1.0, interactive=True) with gr.Group(visible=isinstance(self.whisper_inf, FasterWhisperInference)): with gr.Column(): nb_length_penalty = gr.Number(label="Length Penalty", value=1, info="Exponential length penalty constant.") nb_repetition_penalty = gr.Number(label="Repetition Penalty", value=1, info="Penalty applied to the score of previously generated tokens (set > 1 to penalize).") nb_no_repeat_ngram_size = gr.Number(label="No Repeat N-gram Size", value=0, precision=0, info="Prevent repetitions of n-grams with this size (set 0 to disable).") tb_prefix = gr.Textbox(label="Prefix", value=None, info="Optional text to provide as a prefix for the first window.") cb_suppress_blank = gr.Checkbox(label="Suppress Blank", value=True, info="Suppress blank outputs at the beginning of the sampling.") tb_suppress_tokens = gr.Textbox(label="Suppress Tokens", value="-1", info="List of token IDs to suppress. -1 will suppress a default set of symbols as defined in the model config.json file.") nb_max_initial_timestamp = gr.Number(label="Max Initial Timestamp", value=1.0, info="The initial timestamp cannot be later than this.") cb_word_timestamps = gr.Checkbox(label="Word Timestamps", value=False, info="Extract word-level timestamps using the cross-attention pattern and dynamic time warping, and include the timestamps for each word in each segment.") tb_prepend_punctuations = gr.Textbox(label="Prepend Punctuations", value="\"'“¿([{-", info="If word_timestamps is True, merge these punctuation symbols with the next word.") tb_append_punctuations = gr.Textbox(label="Append Punctuations", value="\"'.。,，!！?？:：”)]}、", info="If word_timestamps is True, merge these punctuation symbols with the previous word.") nb_max_new_tokens = gr.Number(label="Max New Tokens", value=None, precision=0, info="Maximum number of new tokens to generate per-chunk. If not set, the maximum will be set by the default max_length.") nb_chunk_length = gr.Number(label="Chunk Length", value=None, info="The length of audio segments. If it is not None, it will overwrite the default chunk_length of the FeatureExtractor.") nb_hallucination_silence_threshold = gr.Number(label="Hallucination Silence Threshold", value=None, info="When word_timestamps is True, skip silent periods longer than this threshold (in seconds) when a possible hallucination is detected.") tb_hotwords = gr.Textbox(label="Hotwords", value=None, info="Hotwords/hint phrases to provide the model with. Has no effect if prefix is not None.") nb_language_detection_threshold = gr.Number(label="Language Detection Threshold", value=None, info="If the maximum probability of the language tokens is higher than this value, the language is detected.") nb_language_detection_segments = gr.Number(label="Language Detection Segments", value=1, precision=0, info="Number of segments to consider for the language detection.") with gr.Group(visible=isinstance(self.whisper_inf, InsanelyFastWhisperInference)): nb_chunk_length_s = gr.Number(label="Chunk Lengths (sec)", value=30, precision=0) nb_batch_size = gr.Number(label="Batch Size", value=24, precision=0) with gr.Accordion("VAD", open=False): cb_vad_filter = gr.Checkbox(label="Enable Silero VAD Filter", value=False, interactive=True) sd_threshold = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Speech Threshold", value=0.5, info="Lower it to be more sensitive to small sounds.") nb_min_speech_duration_ms = gr.Number(label="Minimum Speech Duration (ms)", precision=0, value=250) nb_max_speech_duration_s = gr.Number(label="Maximum Speech Duration (s)", value=9999) nb_min_silence_duration_ms = gr.Number(label="Minimum Silence Duration (ms)", precision=0, value=2000) nb_speech_pad_ms = gr.Number(label="Speech Padding (ms)", precision=0, value=400) with gr.Accordion("Diarization", open=False): cb_diarize = gr.Checkbox(label="Enable Diarization") tb_hf_token = gr.Text(label="HuggingFace Token", value="", info="This is only needed the first time you download the model. If you already have models, you don't need to enter. " "To download the model, you must manually go to \"https://huggingface.co/pyannote/speaker-diarization-3.1\" and agree to their requirement.") dd_diarization_device = gr.Dropdown(label="Device", choices=self.whisper_inf.diarizer.get_available_device(), value=self.whisper_inf.diarizer.get_device()) with gr.Row(): btn_run = gr.Button("GENERATE SUBTITLE FILE", variant="primary") with gr.Row(): tb_indicator = gr.Textbox(label="Output", scale=5) files_subtitles = gr.Files(label="Downloadable output file", scale=3) btn_openfolder = gr.Button('📂', scale=1) params = [mic_input, dd_file_format] whisper_params = WhisperParameters( model_size=dd_model, lang=dd_lang, is_translate=cb_translate, beam_size=nb_beam_size, log_prob_threshold=nb_log_prob_threshold, no_speech_threshold=nb_no_speech_threshold, compute_type=dd_compute_type, best_of=nb_best_of, patience=nb_patience, condition_on_previous_text=cb_condition_on_previous_text, initial_prompt=tb_initial_prompt, temperature=sd_temperature, compression_ratio_threshold=nb_compression_ratio_threshold, vad_filter=cb_vad_filter, threshold=sd_threshold, min_speech_duration_ms=nb_min_speech_duration_ms, max_speech_duration_s=nb_max_speech_duration_s, min_silence_duration_ms=nb_min_silence_duration_ms, speech_pad_ms=nb_speech_pad_ms, chunk_length_s=nb_chunk_length_s, batch_size=nb_batch_size, is_diarize=cb_diarize, hf_token=tb_hf_token, diarization_device=dd_diarization_device, length_penalty=nb_length_penalty, repetition_penalty=nb_repetition_penalty, no_repeat_ngram_size=nb_no_repeat_ngram_size, prefix=tb_prefix, suppress_blank=cb_suppress_blank, suppress_tokens=tb_suppress_tokens, max_initial_timestamp=nb_max_initial_timestamp, word_timestamps=cb_word_timestamps, prepend_punctuations=tb_prepend_punctuations, append_punctuations=tb_append_punctuations, max_new_tokens=nb_max_new_tokens, chunk_length=nb_chunk_length, hallucination_silence_threshold=nb_hallucination_silence_threshold, hotwords=tb_hotwords, language_detection_threshold=nb_language_detection_threshold, language_detection_segments=nb_language_detection_segments ) btn_run.click(fn=self.whisper_inf.transcribe_mic, inputs=params + whisper_params.as_list(), outputs=[tb_indicator, files_subtitles]) btn_openfolder.click(fn=lambda: self.open_folder("outputs"), inputs=None, outputs=None) dd_model.change(fn=self.on_change_models, inputs=[dd_model], outputs=[cb_translate]) with gr.TabItem("T2T Translation"): # tab 4 with gr.Row(): file_subs = gr.Files(type="filepath", label="Upload Subtitle Files to translate here", file_types=['.vtt', '.srt']) with gr.TabItem("DeepL API"): # sub tab1 with gr.Row(): tb_authkey = gr.Textbox(label="Your Auth Key (API KEY)", value="") with gr.Row(): dd_deepl_sourcelang = gr.Dropdown(label="Source Language", value="Automatic Detection", choices=list( self.deepl_api.available_source_langs.keys())) dd_deepl_targetlang = gr.Dropdown(label="Target Language", value="English", choices=list( self.deepl_api.available_target_langs.keys())) with gr.Row(): cb_deepl_ispro = gr.Checkbox(label="Pro User?", value=False) with gr.Row(): btn_run = gr.Button("TRANSLATE SUBTITLE FILE", variant="primary") with gr.Row(): tb_indicator = gr.Textbox(label="Output", scale=5) files_subtitles = gr.Files(label="Downloadable output file", scale=3) btn_openfolder = gr.Button('📂', scale=1) btn_run.click(fn=self.deepl_api.translate_deepl, inputs=[tb_authkey, file_subs, dd_deepl_sourcelang, dd_deepl_targetlang, cb_deepl_ispro], outputs=[tb_indicator, files_subtitles]) btn_openfolder.click(fn=lambda: self.open_folder(os.path.join("outputs", "translations")), inputs=None, outputs=None) with gr.TabItem("NLLB"): # sub tab2 with gr.Row(): dd_nllb_model = gr.Dropdown(label="Model", value="facebook/nllb-200-1.3B", choices=self.nllb_inf.available_models) dd_nllb_sourcelang = gr.Dropdown(label="Source Language", choices=self.nllb_inf.available_source_langs) dd_nllb_targetlang = gr.Dropdown(label="Target Language", choices=self.nllb_inf.available_target_langs) with gr.Row(): nb_max_length = gr.Number(label="Max Length Per Line", value=200, precision=0) with gr.Row(): cb_timestamp = gr.Checkbox(value=True, label="Add a timestamp to the end of the filename", interactive=True) with gr.Row(): btn_run = gr.Button("TRANSLATE SUBTITLE FILE", variant="primary") with gr.Row(): tb_indicator = gr.Textbox(label="Output", scale=5) files_subtitles = gr.Files(label="Downloadable output file", scale=3) btn_openfolder = gr.Button('📂', scale=1) with gr.Column(): md_vram_table = gr.HTML(NLLB_VRAM_TABLE, elem_id="md_nllb_vram_table") btn_run.click(fn=self.nllb_inf.translate_file, inputs=[file_subs, dd_nllb_model, dd_nllb_sourcelang, dd_nllb_targetlang, nb_max_length, cb_timestamp], outputs=[tb_indicator, files_subtitles]) btn_openfolder.click(fn=lambda: self.open_folder(os.path.join("outputs", "translations")), inputs=None, outputs=None) # Launch the app with optional gradio settings launch_args = {} if self.args.share: launch_args['share'] = self.args.share if self.args.server_name: launch_args['server_name'] = self.args.server_name if self.args.server_port: launch_args['server_port'] = self.args.server_port if self.args.username and self.args.password: launch_args['auth'] = (self.args.username, self.args.password) if self.args.root_path: launch_args['root_path'] = self.args.root_path launch_args['inbrowser'] = True self.app.queue(api_open=False).launch(**launch_args) # Create the parser for command-line arguments parser = argparse.ArgumentParser() parser.add_argument('--whisper_type', type=str, default="faster-whisper", help='A type of the whisper implementation between: ["whisper", "faster-whisper", "insanely-fast-whisper"]') parser.add_argument('--share', type=bool, default=False, nargs='?', const=True, help='Gradio share value') parser.add_argument('--server_name', type=str, default=None, help='Gradio server host') parser.add_argument('--server_port', type=int, default=None, help='Gradio server port') parser.add_argument('--root_path', type=str, default=None, help='Gradio root path') parser.add_argument('--username', type=str, default=None, help='Gradio authentication username') parser.add_argument('--password', type=str, default=None, help='Gradio authentication password') parser.add_argument('--theme', type=str, default=None, help='Gradio Blocks theme') parser.add_argument('--colab', type=bool, default=False, nargs='?', const=True, help='Is colab user or not') parser.add_argument('--api_open', type=bool, default=False, nargs='?', const=True, help='enable api or not') parser.add_argument('--whisper_model_dir', type=str, default=os.path.join("models", "Whisper"), help='Directory path of the whisper model') parser.add_argument('--faster_whisper_model_dir', type=str, default=os.path.join("models", "Whisper", "faster-whisper"), help='Directory path of the faster-whisper model') parser.add_argument('--insanely_fast_whisper_model_dir', type=str, default=os.path.join("models", "Whisper", "insanely-fast-whisper"), help='Directory path of the insanely-fast-whisper model') parser.add_argument('--diarization_model_dir', type=str, default=os.path.join("models", "Diarization"), help='Directory path of the diarization model') parser.add_argument('--nllb_model_dir', type=str, default=os.path.join("models", "NLLB"), help='Directory path of the Facebook NLLB model') parser.add_argument('--output_dir', type=str, default=os.path.join("outputs"), help='Directory path of the outputs') _args = parser.parse_args() if __name__ == "__main__": app = App(args=_args) app.launch()