# -*- coding: utf-8 -*- import numpy as np import soundfile import audresample import text_utils import msinference import re import srt import subprocess import cv2 import markdown import json from pathlib import Path from types import SimpleNamespace from flask import Flask, request, send_from_directory from flask_cors import CORS from moviepy.editor import * from audiocraft.builders import AudioGen CACHE_DIR = 'flask_cache/' NUM_SOUND_GENERATIONS = 1 # batch size to generate same text (same scene for long video) sound_generator = AudioGen(duration=.74, device='cuda:0').to('cuda:0').eval() Path(CACHE_DIR).mkdir(parents=True, exist_ok=True) import nltk nltk.download('punkt') # SSH AGENT # eval $(ssh-agent -s) # ssh-add ~/.ssh/id_ed25519_github2024 # # git remote set-url origin git@github.com:audeering/shift # == 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 _shift(x): n = x.shape[0] i = np.random.randint(.24 * n, max(1, .74 * n)) # high should be above >= 0 x = np.roll(x, i) # we can add the one or fade it and then amplify # the audio is so short 6s that is difficult to not hear the shift somewhere # Just concatenate - raw - and then shift - the longconcat audio - many times may fix it # fade_in = 1 - .5 * np.tanh(-4*(np.linspace(-10, 10, n) - 9.4)) + .5 * np.tanh(4*(np.linspace(-10, 10, n) + 9.4)) return x #* fade_in # silence this def overlay(x, scene=None): if scene is not None: # SOUNDS print(f'AudioGen {NUM_SOUND_GENERATIONS} x {scene}') background = sound_generator.generate( [scene] * NUM_SOUND_GENERATIONS ).reshape(-1).detach().cpu().numpy() # bs, 11400 # upsample 16 kHz AudioGen to 24kHZ StyleTTS print('Resampling') background = audresample.resample( background, original_rate=16000, # sound_generator.sample_rate, target_rate=24000)[0, :] # background /= np.abs(background).max() + 1e-7 Apply in sound_generator() # replicat audiogen to match TTS n_repeat = len(x) // background.shape[0] + 2 # Reach the full length of TTS by cloning print(f'Additional Repeat {n_repeat=}') background = np.concatenate(n_repeat * [background]) # background = _shift(background) print(f'\n====SOUND BACKGROUND SHAPE\n{background.shape=}', f'{np.abs(background.max())=}\n{x.shape=}') x = .1 * x + .9 * background[:len(x)] else: print('sound_background = None') return x def tts_multi_sentence(precomputed_style_vector=None, text=None, voice=None, scene=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) scene : 'A castle in far away lands' -> if passed will generate background sound scene ''' # StyleTTS2 if ('en_US/' in voice) or ('en_UK/' in voice) or (voice is None): assert precomputed_style_vector is not None, 'For affective TTS, style vector is needed.' x = [] for _sentence in text: x.append(msinference.inference(_sentence, precomputed_style_vector, alpha=0.3, beta=0.7, diffusion_steps=7, embedding_scale=1)) x = np.concatenate(x) x /= np.abs(x).max() + 1e-7 # amplify speech to full [-1,1] return overlay(x, scene=scene) # Fallback - Mimic-3 text_utils.store_ssml(text=text, voice=voice) # Text has to be list of single sentences ps = subprocess.Popen(f'cat _tmp_ssml.txt | mimic3 --ssml > _tmp.wav', shell=True) ps.wait() x, fs = soundfile.read('_tmp.wav') x = audresample.resample(x.astype(np.float32), 24000, fs)[0, :] # reshapes (64,) -> (1,64) return overlay(x, scene=scene) # voices = {} # import phonemizer # global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True) app = Flask(__name__) cors = CORS(app) @app.route("/") def index(): with open('README.md', 'r') as f: return markdown.markdown(f.read()) @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}{filename.replace("/","")}') print('Saved all files on Server Side\n\n') args = SimpleNamespace(text=None if r.get('text') is None else CACHE_DIR + r.get('text')[0].replace("/",""), video=None if r.get('video') is None else CACHE_DIR + r.get('video')[0].replace("/",""), image=None if r.get('image') is None else CACHE_DIR + r.get('image')[0].replace("/",""), voice=r.get('voice')[0], native=None if r.get('native') is None else CACHE_DIR + r.get('native')[0].replace("/",""), affective = r.get('affective')[0], scene=r.get('scene')[0] if r.get('scene') 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 text = text_utils.split_into_sentences(t) # split to short sentences (~200 phonemes max) # ====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 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') print('\n STYLE VECTOR \n', precomputed_style_vector.shape) # ====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, scene=args.scene) ) 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, scene=args.scene) 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(STATIC_FRAME).set_duration(5) # as long as the audio - TTS first clip_silent.write_videofile(SILENT_VIDEO, fps=24) x = tts_multi_sentence(text=text, precomputed_style_vector=precomputed_style_vector, voice=args.voice, scene=args.scene ) 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, scene=args.scene) OUT_FILE = 'tmp.wav' soundfile.write(CACHE_DIR + OUT_FILE, x, 24000) # audios = [msinference.inference(text, # msinference.compute_style(f'voices/{voice}.wav'), # alpha=0.3, beta=0.7, diffusion_steps=7, embedding_scale=1)] # # 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")