modules/whisper/whisper_base.py

import os
import torch
import whisper
import gradio as gr
import torchaudio
from abc import ABC, abstractmethod
from typing import BinaryIO, Union, Tuple, List
import numpy as np
from datetime import datetime
from faster_whisper.vad import VadOptions
from dataclasses import astuple
import gc
from copy import deepcopy

from modules.uvr.music_separator import MusicSeparator
from modules.utils.paths import (WHISPER_MODELS_DIR, DIARIZATION_MODELS_DIR, OUTPUT_DIR, DEFAULT_PARAMETERS_CONFIG_PATH,
                                 UVR_MODELS_DIR)
from modules.utils.subtitle_manager import get_srt, get_vtt, get_txt, get_csv, write_file, safe_filename
from modules.utils.youtube_manager import get_ytdata, get_ytaudio
from modules.utils.files_manager import get_media_files, format_gradio_files, load_yaml, save_yaml
from modules.whisper.whisper_parameter import *
from modules.diarize.diarizer import Diarizer
from modules.vad.silero_vad import SileroVAD
from modules.translation.nllb_inference import NLLBInference
from modules.translation.nllb_inference import NLLB_AVAILABLE_LANGS 

class WhisperBase(ABC):
    def __init__(self,
                 model_dir: str = WHISPER_MODELS_DIR,
                 diarization_model_dir: str = DIARIZATION_MODELS_DIR,
                 uvr_model_dir: str = UVR_MODELS_DIR,
                 output_dir: str = OUTPUT_DIR,
                 ):
        self.model_dir = model_dir
        self.output_dir = output_dir
        os.makedirs(self.output_dir, exist_ok=True)
        os.makedirs(self.model_dir, exist_ok=True)
        self.diarizer = Diarizer(
            model_dir=diarization_model_dir
        )
        self.vad = SileroVAD()
        self.music_separator = MusicSeparator(
            model_dir=uvr_model_dir,
            output_dir=os.path.join(output_dir, "UVR")
        )

        self.model = None
        self.current_model_size = None
        self.available_models = whisper.available_models()
        self.available_langs = sorted(list(whisper.tokenizer.LANGUAGES.values()))
        #self.translatable_models = ["large", "large-v1", "large-v2", "large-v3"]
        self.translatable_models = whisper.available_models()
        self.device = self.get_device()
        self.available_compute_types = ["float16", "float32"]
        self.current_compute_type = "float16" if self.device == "cuda" else "float32"

    @abstractmethod
    def transcribe(self,
                   audio: Union[str, BinaryIO, np.ndarray],
                   progress: gr.Progress = gr.Progress(),
                   *whisper_params,
                   ):
        """Inference whisper model to transcribe"""
        pass

    @abstractmethod
    def update_model(self,
                     model_size: str,
                     compute_type: str,
                     progress: gr.Progress = gr.Progress()
                     ):
        """Initialize whisper model"""
        pass

    def run(self,
            audio: Union[str, BinaryIO, np.ndarray],
            progress: gr.Progress = gr.Progress(),
            add_timestamp: bool = True,
            *whisper_params,
            ) -> Tuple[List[dict], float]:
        """
        Run transcription with conditional pre-processing and post-processing.
        The VAD will be performed to remove noise from the audio input in pre-processing, if enabled.
        The diarization will be performed in post-processing, if enabled.

        Parameters
        ----------
        audio: Union[str, BinaryIO, np.ndarray]
            Audio input. This can be file path or binary type.
        progress: gr.Progress
            Indicator to show progress directly in gradio.
        add_timestamp: bool
            Whether to add a timestamp at the end of the filename.
        *whisper_params: tuple
            Parameters related with whisper. This will be dealt with "WhisperParameters" data class

        Returns
        ----------
        segments_result: List[dict]
            list of dicts that includes start, end timestamps and transcribed text
        elapsed_time: float
            elapsed time for running
        """
        
        start_time = datetime.now()
        params = WhisperParameters.as_value(*whisper_params)

        # Get the offload params
        default_params = load_yaml(DEFAULT_PARAMETERS_CONFIG_PATH)        
        whisper_params = default_params["whisper"]
        diarization_params = default_params["diarization"]
        bool_whisper_enable_offload = whisper_params["enable_offload"]
        bool_diarization_enable_offload = diarization_params["enable_offload"]
        
        if params.lang is None:
            pass
        elif params.lang == "Automatic Detection":
            params.lang = None
        else:
            language_code_dict = {value: key for key, value in whisper.tokenizer.LANGUAGES.items()}
            params.lang = language_code_dict[params.lang]

        if params.is_bgm_separate:
            music, audio, _ = self.music_separator.separate(
                audio=audio,
                model_name=params.uvr_model_size,
                device=params.uvr_device,
                segment_size=params.uvr_segment_size,
                save_file=params.uvr_save_file,
                progress=progress
            )

            if audio.ndim >= 2:
                audio = audio.mean(axis=1)
                if self.music_separator.audio_info is None:
                    origin_sample_rate = 16000
                else:
                    origin_sample_rate = self.music_separator.audio_info.sample_rate
                audio = self.resample_audio(audio=audio, original_sample_rate=origin_sample_rate)

            if params.uvr_enable_offload:
                self.music_separator.offload()
            elapsed_time_bgm_sep = datetime.now() - start_time

        origin_audio = deepcopy(audio)
        
        if params.vad_filter:
            # Explicit value set for float('inf') from gr.Number()
            if params.max_speech_duration_s is None or params.max_speech_duration_s >= 9999:
                params.max_speech_duration_s = float('inf')

            progress(0, desc="Filtering silent parts from audio...")
            vad_options = VadOptions(
                threshold=params.threshold,
                min_speech_duration_ms=params.min_speech_duration_ms,
                max_speech_duration_s=params.max_speech_duration_s,
                min_silence_duration_ms=params.min_silence_duration_ms,
                speech_pad_ms=params.speech_pad_ms
            )

            vad_processed, speech_chunks = self.vad.run(
                audio=audio,
                vad_parameters=vad_options,
                progress=progress
            )

            if vad_processed.size > 0:
                audio = vad_processed
            else:
                params.vad_filter = False
        
        result, elapsed_time = self.transcribe(
            audio,
            progress,
            *astuple(params)
        )
        if bool_whisper_enable_offload:
            self.offload()

        if params.vad_filter:
            restored_result = self.vad.restore_speech_timestamps(
                segments=result,
                speech_chunks=speech_chunks,
            )
            if restored_result:
                result = restored_result
            else:
                print("VAD detected no speech segments in the audio.")

        if params.is_diarize:
            progress(0.99, desc="Diarizing speakers...")
            result, elapsed_time_diarization = self.diarizer.run(
                audio=origin_audio,
                use_auth_token=params.hf_token,
                transcribed_result=result,
                device=params.diarization_device
            )
            if bool_diarization_enable_offload:
                self.diarizer.offload()
        
        if not result:
            print(f"Whisper did not detected any speech segments in the audio.")
            result = list()

        progress(1.0, desc="Processing done!")
        total_elapsed_time = datetime.now() - start_time
        return result, elapsed_time

    def transcribe_file(self,
                        files: Optional[List] = None,
                        input_folder_path: Optional[str] = None,
                        file_format: str = "SRT",
                        add_timestamp: bool = True,
                        translate_output: bool = False,
                        translate_model: str = "",
                        target_lang: str = "",
                        progress=gr.Progress(),
                        *whisper_params,
                        ) -> list:
        """
        Write subtitle file from Files

        Parameters
        ----------
        files: list
            List of files to transcribe from gr.Files()
        input_folder_path: str
            Input folder path to transcribe from gr.Textbox(). If this is provided, `files` will be ignored and
            this will be used instead.
        file_format: str
            Subtitle File format to write from gr.Dropdown(). Supported format: [SRT, WebVTT, txt]
        add_timestamp: bool
            Boolean value from gr.Checkbox() that determines whether to add a timestamp at the end of the subtitle filename.
        translate_output: bool
            Translate output
        translate_model: str
            Translation model to use
        target_lang: str
            Target language to use
        progress: gr.Progress
            Indicator to show progress directly in gradio.
        *whisper_params: tuple
            Parameters related with whisper. This will be dealt with "WhisperParameters" data class

        Returns
        ----------
        result_str:
            Result of transcription to return to gr.Textbox()
        result_file_path:
            Output file path to return to gr.Files()
        """
        try:
            if input_folder_path:
                files = get_media_files(input_folder_path)
            if isinstance(files, str):
                files = [files]
            if files and isinstance(files[0], gr.utils.NamedString):
                files = [file.name for file in files]

            ## Initialization variables & start time
            files_info = {}
            files_to_download = {}
            time_start = datetime.now()
            
            ## Load parameters related with whisper
            params = WhisperParameters.as_value(*whisper_params)
            
            ## Load model to detect language
            model = whisper.load_model("base")
            
            for file in files:
    
                ## Detect language
                mel = whisper.log_mel_spectrogram(whisper.pad_or_trim(whisper.load_audio(file))).to(model.device)
                _, probs = model.detect_language(mel)
                file_language = ""
                file_lang_probs = ""
                for key,value in whisper.tokenizer.LANGUAGES.items():
                    if key == str(max(probs, key=probs.get)):
                        file_language = value.capitalize()
                        for key_prob,value_prob in probs.items():
                            if key == key_prob:
                                file_lang_probs = str((round(value_prob*100)))
                                break
                        break
                                
                transcribed_segments, time_for_task = self.run(
                    file,
                    progress,
                    add_timestamp,
                    *whisper_params,
                )
                
                # Define source language
                source_lang = file_language
                
                # Translate to English using Whisper built-in functionality
                transcription_note = ""
                if params.is_translate:
                    if source_lang != "English":
                        transcription_note = "To English"
                        source_lang = "English"
                    else:
                        transcription_note = "Already in English"
                
                # Translate the transcribed segments
                translation_note = ""
                if translate_output:
                    if source_lang != target_lang:
                        self.nllb_inf = NLLBInference()
                        if source_lang in NLLB_AVAILABLE_LANGS.keys():
                            transcribed_segments = self.nllb_inf.translate_text(
                                input_list_dict=transcribed_segments,
                                model_size=translate_model,
                                src_lang=source_lang,
                                tgt_lang=target_lang,
                                speaker_diarization=params.is_diarize
                            )
                            translation_note = "To " + target_lang
                        else:
                            translation_note = source_lang + " not supported"
                    else:
                        translation_note = "Already in " + target_lang
                
                ## Get preview as txt
                file_name, file_ext = os.path.splitext(os.path.basename(file))
                subtitle = get_txt(transcribed_segments)
                files_info[file_name] = {"subtitle": subtitle, "time_for_task": time_for_task, "lang": file_language, "lang_prob": file_lang_probs, "input_source_file": (file_name+file_ext), "translation": translation_note, "transcription": transcription_note}
                
                ## Add output file as txt
                file_name, file_ext = os.path.splitext(os.path.basename(file))
                subtitle, file_path = self.generate_and_write_file(
                    file_name=file_name,
                    transcribed_segments=transcribed_segments,
                    add_timestamp=add_timestamp,
                    file_format="txt",
                    output_dir=self.output_dir
                )
                files_to_download[file_name+"_txt"] = {"path": file_path}
                
                ## Add output file as srt
                file_name, file_ext = os.path.splitext(os.path.basename(file))
                subtitle, file_path = self.generate_and_write_file(
                    file_name=file_name,
                    transcribed_segments=transcribed_segments,
                    add_timestamp=add_timestamp,
                    file_format="srt",
                    output_dir=self.output_dir
                )
                files_to_download[file_name+"_srt"] = {"path": file_path}

                ## Add output file as csv
                file_name, file_ext = os.path.splitext(os.path.basename(file))
                subtitle, file_path = self.generate_and_write_file(
                    file_name=file_name,
                    transcribed_segments=transcribed_segments,
                    add_timestamp=add_timestamp,
                    file_format="csv",
                    output_dir=self.output_dir
                )
                files_to_download[file_name+"_csv"] = {"path": file_path}
            
            total_result = ''
            total_info = ''
            total_time = 0
            for file_name, info in files_info.items():
                total_result += f'{info["subtitle"]}'
                total_time += info["time_for_task"]
                total_info += f'Input file:\t\t{info["input_source_file"]}\nLanguage:\t{info["lang"]} (probability {info["lang_prob"]}%)\n'
                
                if params.is_translate:
                    total_info += f'Translation:\t{info["transcription"]}\n\t⤷ Handled by OpenAI Whisper\n'
                
                if translate_output:
                    total_info += f'Translation:\t{info["translation"]}\n\t⤷ Handled by Facebook NLLB\n'
            
            time_end = datetime.now()
            total_info += f"\nTotal processing time: {self.format_time((time_end-time_start).total_seconds())}"

            result_str = total_result.rstrip("\n")
            result_file_path = [info['path'] for info in files_to_download.values()]
    
            return [result_str,result_file_path,total_info]
        
        except Exception as e:
            print(f"Error transcribing file: {e}")
        finally:
            self.release_cuda_memory()
    
    def transcribe_mic(self,
                       mic_audio: str,
                       file_format: str = "SRT",
                       add_timestamp: bool = True,
                       progress=gr.Progress(),
                       *whisper_params,
                       ) -> list:
        """
        Write subtitle file from microphone

        Parameters
        ----------
        mic_audio: str
            Audio file path from gr.Microphone()
        file_format: str
            Subtitle File format to write from gr.Dropdown(). Supported format: [SRT, WebVTT, txt]
        add_timestamp: bool
            Boolean value from gr.Checkbox() that determines whether to add a timestamp at the end of the filename.
        progress: gr.Progress
            Indicator to show progress directly in gradio.
        *whisper_params: tuple
            Parameters related with whisper. This will be dealt with "WhisperParameters" data class

        Returns
        ----------
        result_str:
            Result of transcription to return to gr.Textbox()
        result_file_path:
            Output file path to return to gr.Files()
        """
        try:
            progress(0, desc="Loading Audio...")
            transcribed_segments, time_for_task = self.run(
                mic_audio,
                progress,
                add_timestamp,
                *whisper_params,
            )
            progress(1, desc="Completed!")

            subtitle, result_file_path = self.generate_and_write_file(
                file_name="Mic",
                transcribed_segments=transcribed_segments,
                add_timestamp=add_timestamp,
                file_format=file_format,
                output_dir=self.output_dir
            )

            result_str = f"Done in {self.format_time(time_for_task)}! Subtitle file is in the outputs folder.\n\n{subtitle}"
            return [result_str, result_file_path]
        except Exception as e:
            print(f"Error transcribing file: {e}")
        finally:
            self.release_cuda_memory()

    def transcribe_youtube(self,
                           youtube_link: str,
                           file_format: str = "SRT",
                           add_timestamp: bool = True,
                           progress=gr.Progress(),
                           *whisper_params,
                           ) -> list:
        """
        Write subtitle file from Youtube

        Parameters
        ----------
        youtube_link: str
            URL of the Youtube video to transcribe from gr.Textbox()
        file_format: str
            Subtitle File format to write from gr.Dropdown(). Supported format: [SRT, WebVTT, txt]
        add_timestamp: bool
            Boolean value from gr.Checkbox() that determines whether to add a timestamp at the end of the filename.
        progress: gr.Progress
            Indicator to show progress directly in gradio.
        *whisper_params: tuple
            Parameters related with whisper. This will be dealt with "WhisperParameters" data class

        Returns
        ----------
        result_str:
            Result of transcription to return to gr.Textbox()
        result_file_path:
            Output file path to return to gr.Files()
        """
        try:
            progress(0, desc="Loading Audio from Youtube...")
            yt = get_ytdata(youtube_link)
            audio = get_ytaudio(yt)

            transcribed_segments, time_for_task = self.run(
                audio,
                progress,
                add_timestamp,
                *whisper_params,
            )

            progress(1, desc="Completed!")

            file_name = safe_filename(yt.title)
            subtitle, result_file_path = self.generate_and_write_file(
                file_name=file_name,
                transcribed_segments=transcribed_segments,
                add_timestamp=add_timestamp,
                file_format=file_format,
                output_dir=self.output_dir
            )
            result_str = f"Done in {self.format_time(time_for_task)}! Subtitle file is in the outputs folder.\n\n{subtitle}"

            if os.path.exists(audio):
                os.remove(audio)

            return [result_str, result_file_path]

        except Exception as e:
            print(f"Error transcribing file: {e}")
        finally:
            self.release_cuda_memory()

    @staticmethod
    def generate_and_write_file(file_name: str,
                                transcribed_segments: list,
                                add_timestamp: bool,
                                file_format: str,
                                output_dir: str
                                ) -> str:
        """
        Writes subtitle file

        Parameters
        ----------
        file_name: str
            Output file name
        transcribed_segments: list
            Text segments transcribed from audio
        add_timestamp: bool
            Determines whether to add a timestamp to the end of the filename.
        file_format: str
            File format to write. Supported formats: [SRT, WebVTT, txt, csv]
        output_dir: str
            Directory path of the output

        Returns
        ----------
        content: str
            Result of the transcription
        output_path: str
            output file path
        """
        if add_timestamp:
            #timestamp = datetime.now().strftime("%m%d%H%M%S")
            timestamp = datetime.now().strftime("%Y%m%d %H%M%S")
            output_path = os.path.join(output_dir, f"{file_name} - {timestamp}")
        else:
            output_path = os.path.join(output_dir, f"{file_name}")

        file_format = file_format.strip().lower()
        if file_format == "srt":
            content = get_srt(transcribed_segments)
            output_path += '.srt'

        elif file_format == "webvtt":
            content = get_vtt(transcribed_segments)
            output_path += '.vtt'

        elif file_format == "txt":
            content = get_txt(transcribed_segments)
            output_path += '.txt'

        elif file_format == "csv":
            content = get_csv(transcribed_segments)
            output_path += '.csv'
        
        write_file(content, output_path)
        return content, output_path

    def offload(self):
        """Offload the model and free up the memory"""
        if self.model is not None:
            del self.model
            self.model = None
        if self.device == "cuda":
            self.release_cuda_memory()
        gc.collect()
    
    @staticmethod
    def format_time(elapsed_time: float) -> str:
        """
        Get {hours} {minutes} {seconds} time format string

        Parameters
        ----------
        elapsed_time: str
            Elapsed time for transcription

        Returns
        ----------
        Time format string
        """
        hours, rem = divmod(elapsed_time, 3600)
        minutes, seconds = divmod(rem, 60)

        time_str = ""

        hours = round(hours)
        if hours:
            if hours == 1:
                time_str += f"{hours} hour "
            else:
                time_str += f"{hours} hours "

        minutes = round(minutes)
        if minutes:
            if minutes == 1:
                time_str += f"{minutes} minute "
            else:
                time_str += f"{minutes} minutes "
        
        seconds = round(seconds)
        if seconds == 1:
            time_str += f"{seconds} second"
        else:
            time_str += f"{seconds} seconds"
        
        return time_str.strip()

    @staticmethod
    def get_device():
        if torch.cuda.is_available():
            return "cuda"
        elif torch.backends.mps.is_available():
            if not WhisperBase.is_sparse_api_supported():
                # Device `SparseMPS` is not supported for now. See : https://github.com/pytorch/pytorch/issues/87886
                return "cpu"
            return "mps"
        else:
            return "cpu"

    @staticmethod
    def is_sparse_api_supported():
        if not torch.backends.mps.is_available():
            return False

        try:
            device = torch.device("mps")
            sparse_tensor = torch.sparse_coo_tensor(
                indices=torch.tensor([[0, 1], [2, 3]]),
                values=torch.tensor([1, 2]),
                size=(4, 4),
                device=device
            )
            return True
        except RuntimeError:
            return False

    @staticmethod
    def release_cuda_memory():
        """Release memory"""
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()

    @staticmethod
    def remove_input_files(file_paths: List[str]):
        """Remove gradio cached files"""
        if not file_paths:
            return

        for file_path in file_paths:
            if file_path and os.path.exists(file_path):
                os.remove(file_path)

    @staticmethod
    def cache_parameters(
        whisper_params: WhisperValues,
        add_timestamp: bool
    ):
        """cache parameters to the yaml file"""
        cached_params = load_yaml(DEFAULT_PARAMETERS_CONFIG_PATH)
        cached_whisper_param = whisper_params.to_yaml()
        cached_yaml = {**cached_params, **cached_whisper_param}
        cached_yaml["whisper"]["add_timestamp"] = add_timestamp

        save_yaml(cached_yaml, DEFAULT_PARAMETERS_CONFIG_PATH)

    @staticmethod
    def resample_audio(audio: Union[str, np.ndarray],
                       new_sample_rate: int = 16000,
                       original_sample_rate: Optional[int] = None,) -> np.ndarray:
        """Resamples audio to 16k sample rate, standard on Whisper model"""
        if isinstance(audio, str):
            audio, original_sample_rate = torchaudio.load(audio)
        else:
            if original_sample_rate is None:
                raise ValueError("original_sample_rate must be provided when audio is numpy array.")
            audio = torch.from_numpy(audio)
        resampler = torchaudio.transforms.Resample(orig_freq=original_sample_rate, new_freq=new_sample_rate)
        resampled_audio = resampler(audio).numpy()
        return resampled_audio