api.py

# -*- coding: utf-8 -*-
import numpy as np
import soundfile
from Utils.text_utils import split_into_sentences
import msinference
import re
import srt
import subprocess
import cv2
from pathlib import Path
from types import SimpleNamespace
from flask import Flask, request, send_from_directory
from moviepy.video.io.VideoFileClip import VideoFileClip
from moviepy.video.VideoClip import ImageClip
from audiocraft.builders import AudioGen

CACHE_DIR = 'flask_cache/'
PIECE_OF_SOUND_DURATION = 4.74 # seconds 
sound_generator = AudioGen(
    duration=PIECE_OF_SOUND_DURATION
                            ).to('cuda:0').eval()

Path(CACHE_DIR).mkdir(parents=True, exist_ok=True)

def _shorten(filename):
    return filename.replace("/","")[-6:]

def _resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    '''https://github.com/PyImageSearch/imutils/blob/master/imutils/convenience.py'''
    # initialize the dimensions of the image to be resized and
    # grab the image size
    dim = None
    (h, w) = image.shape[:2]

    # if both the width and height are None, then return the
    # original image
    if width is None and height is None:
        return image

    # check to see if the width is None
    if width is None:
        # calculate the ratio of the height and construct the
        # dimensions
        r = height / float(h)
        dim = (int(w * r), height)

    # otherwise, the height is None
    else:
        # calculate the ratio of the width and construct the
        # dimensions
        r = width / float(w)
        dim = (width, int(h * r))

    # resize the image
    resized = cv2.resize(image, dim, interpolation=inter)

    # return the resized image
    return resized

def overlay(x, soundscape=None):
    # pre-calculate the n_repeat here then apply torchaudio.resample and repeat insd sound_gen forward()
    if soundscape is not None:
        
        background = sound_generator.generate(soundscape,
            n_repeat=int(len(x) / (PIECE_OF_SOUND_DURATION * 16000)) + 1
                                        ).detach().cpu().numpy() # bs, 11400 @.74s
        
        # blend TTS
        
        x = .5 * x + .5 * background[:len(x)]
        
    else:
        
        print('sound_background = None')
        
    return x


def tts_multi_sentence(precomputed_style_vector=None,
                       text=None,
                       voice=None,
                       soundscape=None,
                       speed=None):
    '''create 24kHZ np.array with tts

       precomputed_style_vector :   required if en_US or en_UK in voice, so
                                    to perform affective TTS.
       text  : string
       voice : string or None (falls to styleTTS)
       soundscape : 'A castle in far away lands' -> if passed will generate background sound soundscape
       '''
    
        
    # StyleTTS2 - English
    
    if precomputed_style_vector is not None:
        x = []
        for _sentence in text:
            
            # StyleTTS2 - pronounciation Fx
            
            _sentence = _sentence.lower()  # .replace("ţ", "ț").replace('ț','ts').replace('î', 'u')
            if 'vctk_low#p326' in voice:
                # fix sounding of sleepy AAABS TRAACT
                _sentence = _sentence.replace('abstract', 'ahbstract')  # 'ahstract'
            x.append(msinference.inference(_sentence,
                        precomputed_style_vector)
                     )
        x = np.concatenate(x)
                    
    # Fallback - MMS TTS - Non-English
    
    else:
        
        # dont split foreign sentences: Avoids re-load of VITS & random speaker change issue
        x = msinference.foreign(text=text,
                                lang=voice,  # voice = 'romanian', 'serbian' 'hungarian'
                                speed=speed)  # normalisation externally
        
        
    # volume
    
    x /= np.abs(x).max() + 1e-7  # amplify speech to full [-1,1]        
        
    return overlay(x, soundscape=soundscape)
    



# voices = {}
# import phonemizer
# global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True,  with_stress=True)

app = Flask(__name__)

@app.route("/", methods=['GET', 'POST', 'PUT'])
def serve_wav():
    # https://stackoverflow.com/questions/13522137/in-flask-convert-form-post-
    #                      object-into-a-representation-suitable-for-mongodb
    r = request.form.to_dict(flat=False)
    
    
    # Physically Save Client Files
    for filename, obj in request.files.items():
        obj.save(f'{CACHE_DIR}{_shorten(filename)}')
        
    print('Saved all files on Server Side\n\n') 

    args = SimpleNamespace(
        text      = None if r.get('text')  is None else CACHE_DIR + _shorten(r.get('text' )[0]),  # crop last letters from original filename & use as tmp
        video     = None if r.get('video') is None else CACHE_DIR + _shorten(r.get('video')[0]),
        image     = None if r.get('image') is None else CACHE_DIR + _shorten(r.get('image')[0]),
        native    = None if r.get('native') is None else CACHE_DIR + _shorten(r.get('native')[0]),
        affective =       r.get('affective')[0],
        voice     =       r.get('voice')[0],
        speed     = float(r.get('speed')[0]),  # For Non-English MMS TTS
        soundscape=r.get('soundscape')[0] if r.get('soundscape') is not None else None,
                )
    # print('\n==RECOMPOSED as \n',request.data,request.form,'\n==')
    

    print(args, 'ENTER Script')
    do_video_dub = True if args.text.endswith('.srt') else False

    SILENT_VIDEO = '_silent_video.mp4'
    AUDIO_TRACK = '_audio_track.wav'

    if do_video_dub:
        print('==\nFound .srt : {args.txt}, thus Video should be given as well\n\n')
        with open(args.text, "r") as f:
            s = f.read()
        text = [[j.content, j.start.total_seconds(), j.end.total_seconds()] for j in srt.parse(s)]
        assert args.video is not None
        native_audio_file = '_tmp.wav'
        subprocess.call(
            ["ffmpeg",
                "-y",  # https://stackoverflow.com/questions/39788972/ffmpeg-overwrite-output-file-if-exists
                "-i",
                args.video,
                "-f",
                "mp3",
                "-ar",
                "24000",  # "22050 for mimic3",
                "-vn",
                native_audio_file])
        x_native, _ = soundfile.read(native_audio_file)  # reads mp3
        x_native = x_native[:, 0]  # stereo
        # ffmpeg -i Sandra\ Kotevska\,\ Painting\ Rose\ bush\,\ mixed\ media\,\ 2017.\ \[NMzC_036MtE\].mkv -f mp3 -ar 22050 -vn out44.wa
    else:
        with open(args.text, 'r') as f:
            t = ''.join(f)
        t = re.sub(' +', ' ', t)  # delete spaces
        # -- sub all punctuation with ' '
        text = split_into_sentences(t)  # split to short sentences (~100 phonemes max for OOM)
        
    # ====STYLE VECTOR====

    precomputed_style_vector = None
    
    if args.native:  # Voice Cloning
        try:
            precomputed_style_vector = msinference.compute_style(args.native)
        except soundfile.LibsndfileError:  # Fallback - internal voice
            print('\n  Could not voice clone audio:', args.native, 'fallback to video or Internal TTS voice.\n')
        if do_video_dub:  # Clone voice via Video
            native_audio_file = args.video.replace('.', '').replace('/', '')
            native_audio_file += '__native_audio_track.wav'
            soundfile.write('tgt_spk.wav',
                np.concatenate([
                    x_native[:int(4 * 24000)]], 0).astype(np.float32), 24000)  # 27400?
            precomputed_style_vector = msinference.compute_style('tgt_spk.wav')

    # NOTE: style vector may be None
    
    # Native Eng

    if precomputed_style_vector is None:
        
        if 'en_US' in args.voice or 'en_UK' in args.voice:
            _dir = '/' if args.affective else '_v2/'
            precomputed_style_vector = msinference.compute_style(
                'assets/wavs/style_vector' + _dir + args.voice.replace(
                    '/', '_').replace(
                    '#', '_').replace(
                    'cmu-arctic', 'cmu_arctic').replace(
                    '_low', '') + '.wav')
        
        # Non-Native Eng
        
        elif '_' in  args.voice:
            precomputed_style_vector = msinference.compute_style('assets/wavs/mimic3_foreign_4x/' + args.voice.replace(
                                                                 '/', '_').replace('#', '_').replace(
                                                                    'cmu-arctic', 'cmu_arctic').replace(
                                                                        '_low', '') + '.wav')
            
        
        # Foreign Lang - MMS/TTS 
        else:
            print(f'\n\n\n\n\n FallBack to MMS TTS due to: {args.voice=}')
        
        
    # precomputed_style_vector is None for Foreign langs
    # ====SILENT VIDEO====

    if args.video is not None:
        # banner - precomput @ 1920 pixels
        frame_tts = np.zeros((104, 1920, 3), dtype=np.uint8)
        font                   = cv2.FONT_HERSHEY_SIMPLEX
        bottomLeftCornerOfText = (240, 74)  # w,h
        fontScale              = 2
        fontColor              = (255, 255, 255)
        thickness              = 4
        lineType               = 2
        cv2.putText(frame_tts, 'TTS',
            bottomLeftCornerOfText,
            font,
            fontScale,
            fontColor,
            thickness,
            lineType)
        #     cv2.imshow('i', frame_tts); cv2.waitKey(); cv2.destroyAllWindows()
        # ====================================== NATIVE VOICE
        frame_orig = np.zeros((104, 1920, 3), dtype=np.uint8)
        font                   = cv2.FONT_HERSHEY_SIMPLEX
        bottomLeftCornerOfText = (101, 74)  # w,h
        fontScale              = 2
        fontColor              = (255, 255, 255)
        thickness              = 4
        lineType               = 1000
        cv2.putText(frame_orig, 'ORIGINAL VOICE',
            bottomLeftCornerOfText,
            font,
            fontScale,
            fontColor,
            thickness,
            lineType)
        
        print(f'\n______________________________\n'
              f'Gen Banners for TTS/Native Title {frame_tts.shape=} {frame_orig.shape=}'
              f'\n______________________________\n')
        # ====SILENT VIDEO EXTRACT====
        # DONLOAD SRT from youtube
        #
        #     yt-dlp --write-sub --sub-lang en --convert-subs "srt" https://www.youtube.com/watch?v=F1Ib7TAu7eg&list=PL4x2B6LSwFewdDvRnUTpBM7jkmpwouhPv&index=2
        #
        #
        # .mkv ->.mp4 moviepy loads only .mp4
        #
        #     ffmpeg -y -i Distaff\ \[qVonBgRXcWU\].mkv -c copy -c:a aac Distaff_qVonBgRXcWU.mp4
        #           video_file, srt_file = ['assets/Head_of_fortuna.mp4',
        #                         'assets/head_of_fortuna_en.srt']
        #
        video_file = args.video
        vf = VideoFileClip(video_file)
        
        # GET 1st FRAME to OBTAIN frame RESOLUTION
        h, w, _ = vf.get_frame(0).shape
        frame_tts = _resize(frame_tts, width=w)
        frame_orig = _resize(frame_orig, width=w)
        h, w, _ = frame_orig.shape
        
        try:
            
            # inpaint banner to say if native voice
            num = x_native.shape[0]
            is_tts = .5 + .5 * np.tanh(4*(np.linspace(-10, 10, num) + 9.4))  # fade heaviside
            
            def inpaint_banner(get_frame, t):
                '''blend banner - (now plays) tts or native voic
                '''
                
                im = np.copy(get_frame(t))  # pic
                

                ix = int(t * 24000)

                if is_tts[ix] > .5:     # mask == 1 => tts / mask == 0 -> native
                    frame = frame_tts   # rename frame to rsz_frame_... because if frame_tts is mod
                                        # then is considered a "local variable" thus the "outer var"
                                        # is not observed by python raising referenced before assign
                else:
                    frame = frame_orig
                
                # im[-h:, -w:, :] = (.4 * im[-h:, -w:, :] + .6 * frame_orig).astype(np.uint8)
                
                

                offset_h = 24
                
                
                print(f'  > inpaint_banner() HAS NATIVE:  {frame.shape=} {im.shape=}\n\n\n\n')
                
                
                
                im[offset_h:h + offset_h, :w, :] = (.4 * im[offset_h:h + offset_h, :w, :] 
                                                    + .6 * frame).astype(np.uint8)
                
                # im2 = np.concatenate([im, frame_tts], 0)
                # cv2.imshow('t', im2); cv2.waitKey(); cv2.destroyAllWindows()
                return im  # np.concatenate([im, frane_ttts], 0)
            
        except UnboundLocalError:  # args.native == False
            
            def inpaint_banner(get_frame, t):
                
                im = np.copy(get_frame(t))
                
                h, w, _ = frame_tts.shape      # frame = banner
                if w != im.shape[1]:        # rsz banners to fit video w
                    local_frame = _resize(frame_tts, width=im.shape[1])
                offset_h = 24
                im[offset_h:h + offset_h, :w, :] = (.4 * im[offset_h:h+offset_h, :w, :] 
                                                    + .6 * local_frame).astype(np.uint8)
                return im
        vf = vf.fl(inpaint_banner)
        vf.write_videofile(SILENT_VIDEO)

        # ==== TTS .srt ====

        if do_video_dub:
            OUT_FILE = 'tmp.mp4' #args.out_file + '_video_dub.mp4'
            subtitles = text
            MAX_LEN = int(subtitles[-1][2] + 17) * 24000  
            # 17 extra seconds fail-safe for long-last-segment
            print("TOTAL LEN SAMPLES ", MAX_LEN, '\n====================')
            pieces = []
            for k, (_text_, orig_start, orig_end) in enumerate(subtitles):

                # PAUSES ?????????????????????????


                pieces.append(tts_multi_sentence(text=[_text_],
                                                 precomputed_style_vector=precomputed_style_vector,
                                                 voice=args.voice,
                                                 soundscape=args.soundscape,
                                                 speed=args.speed)
                              )
            total = np.concatenate(pieces, 0)
            # x = audresample.resample(x.astype(np.float32), 24000, 22050)  # reshapes (64,) -> (1,64)
            # PAD SHORTEST of  TTS / NATIVE
            if len(x_native) > len(total):
                total = np.pad(total, (0, max(0, x_native.shape[0] - total.shape[0])))

            else:  # pad native to len of is_tts & total
                x_native = np.pad(x_native, (0, max(0, total.shape[0] - x_native.shape[0])))
            # print(total.shape, x_native.shape, 'PADDED TRACKS')
            soundfile.write(AUDIO_TRACK,
                            # (is_tts * total + (1-is_tts) * x_native)[:, None],
                            (.64 * total + .27 * x_native)[:, None],
                            24000)
        else:  # Video from plain (.txt)
            OUT_FILE = 'tmp.mp4'
            x = tts_multi_sentence(text=text,
                               precomputed_style_vector=precomputed_style_vector,
                               voice=args.voice,
                               soundscape=args.soundscape,
                               speed=args.speed)
            soundfile.write(AUDIO_TRACK, x, 24000)

    # IMAGE 2 SPEECH

    if args.image is not None:

        STATIC_FRAME = args.image  # 'assets/image_from_T31.jpg'
        OUT_FILE = 'tmp.mp4' #args.out_file + '_image_to_speech.mp4'

        # SILENT CLIP

        clip_silent = ImageClip(img=STATIC_FRAME,
                                duration=5)  # ffmpeg continues this silent video for duration of TTS
        clip_silent.write_videofile(SILENT_VIDEO, fps=24)

        x = tts_multi_sentence(text=text,
                               precomputed_style_vector=precomputed_style_vector,
                               voice=args.voice,
                               soundscape=args.soundscape,
                               speed=args.speed
                               )
        soundfile.write(AUDIO_TRACK, x, 24000)
    if args.video or args.image:
        # write final output video
        subprocess.call(
            ["ffmpeg",
                "-y",
                "-i",
                SILENT_VIDEO,
                "-i",
                AUDIO_TRACK,
                "-c:v",
                "copy",
                "-map",
                "0:v:0",
                "-map",
                " 1:a:0",
                CACHE_DIR + OUT_FILE])

        print(f'\noutput video is saved as {OUT_FILE}')
        
    else:
        
        # Fallback: No image nor video provided - do only tts
        x = tts_multi_sentence(text=text,
                               precomputed_style_vector=precomputed_style_vector, 
                               voice=args.voice,
                               soundscape=args.soundscape,
                               speed=args.speed)
        OUT_FILE = 'tmp.wav'
        soundfile.write(CACHE_DIR + OUT_FILE, x, 24000)


    

    # audios = [msinference.inference(text, 
    #                                 msinference.compute_style(f'voices/{voice}.wav'))]
    # # for t in [text]:
    # output_buffer = io.BytesIO()
    # write(output_buffer, 24000, np.concatenate(audios))
    # response = Response(output_buffer.getvalue())
    # response.headers["Content-Type"] = "audio/wav"
    # https://stackoverflow.com/questions/67591467/
    #            flask-shows-typeerror-send-from-directory-missing-1-required-positional-argum
    
    
    
    # send server's output as default file -> srv_result.xx
    print(f'\n=SERVER saved as {OUT_FILE=}\n')
    response = send_from_directory(CACHE_DIR, path=OUT_FILE)
    response.headers['suffix-file-type'] = OUT_FILE
    print('________________\n              ? \n_______________')
    return response


if __name__ == "__main__":
    app.run(host="0.0.0.0")