# Synthesize all Harvard Lists 77x lists of 10x sentences to single .wav ----- NEEDS TO BE RUN from https://github.com/audeering/shift/
#
# 1. using mimic3 style
#     Folder: 'prompt_mimic3/'
# 2. using mimic3 4x accelerated style
#     Folder: 'prompt_mimic3speed/'
# 3. using crema-d style
#     Folder: 'prompt_human/'
#
# WAVS used from  tts_paper_plot.py

import soundfile
import json
import numpy as np
import audb
from pathlib import Path

LABELS = ['arousal', 'dominance', 'valence']




def load_speech(split=None):
    DB = [
        # [dataset, version, table, has_timdeltas_or_is_full_wavfile]
           ['crema-d', '1.1.1', 'emotion.voice.test', False],
 #           ['emodb',  '1.2.0', 'emotion.categories.train.gold_standard', False],
  #          ['entertain-playtestcloud', '1.1.0', 'emotion.categories.train.gold_standard', True],
   #         ['erik', '2.2.0', 'emotion.categories.train.gold_standard', True],
    #        ['meld', '1.3.1', 'emotion.categories.train.gold_standard', False],
            # ['msppodcast', '5.0.0', 'emotion.categories.train.gold_standard', False],  # tandalone bucket because it has gt labels?
     #       ['myai', '1.0.1', 'emotion.categories.train.gold_standard', False],
      #      ['casia', None, 'emotion.categories.gold_standard', False],
            # ['switchboard-1', None, 'sentiment', True],
            # ['swiss-parliament', None, 'segments', True], 
            # ['argentinian-parliament', None, 'segments', True],
            # ['austrian-parliament', None, 'segments', True],
            # #'german', --> bundestag
            # ['brazilian-parliament', None, 'segments', True],
            # ['mexican-parliament', None, 'segments', True],
            # ['portuguese-parliament', None, 'segments', True],
       #     ['spanish-parliament', None, 'segments', True],
        #    ['chinese-vocal-emotions-liu-pell', None, 'emotion.categories.desired', False],
            # peoples-speech slow
         #   ['peoples-speech', None, 'train-initial', False]
    ]

    output_list = []
    for database_name, ver, table, has_timedeltas in DB:

        a = audb.load(database_name,
                        sampling_rate=16000,
                        format='wav',
                        mixdown=True,
                        version=ver,
                        cache_root='/cache/audb/')
        a = a[table].get()
        if has_timedeltas:
            print(f'{has_timedeltas=}')
            # a = a.reset_index()[['file', 'start', 'end']]
            # output_list += [[*t] for t
            #         in zip(a.file.values, a.start.dt.total_seconds().values, a.end.dt.total_seconds().values)]
        else:
            output_list += [f for f in a.index]  # use file (no timedeltas)
    return output_list





# Generate 77 wavs




with open('voices.json', 'r') as f:
    df = json.load(f)['voices']
voice_names = [v['voice'] for k,v in df.items()]
synthetic_wav_paths = []
synthetic_wav_paths_AFFECT = []
for voice in voice_names:
    
    synthetic_wav_paths.append(
      'assets/wavs/style_vector/' + voice.replace('/', '_').replace('#', '_').replace(
                    'cmu-arctic', 'cmu_arctic').replace('_low', '') + '.wav')
    synthetic_wav_paths_AFFECT.append(
        'assets/wavs/style_vector_v2/' + voice.replace('/', '_').replace('#', '_').replace(
                    'cmu-arctic', 'cmu_arctic').replace('_low', '') + '.wav')


print(len(synthetic_wav_paths))

    
natural_wav_paths = load_speech()


# SYNTHESIZE mimic mimicx4 crema-d
import msinference


with open('harvard.json', 'r') as f:
    harvard_individual_sentences = json.load(f)['sentences']





for audio_prompt in ['mimic3', 'mimic3_speed', 'human']:
    total_audio = []
    ix = 0
    for list_of_10 in harvard_individual_sentences:
        # long_sentence = ' '.join(list_of_10['sentences'])
        # harvard.append(long_sentence.replace('.', ' '))
        for text in list_of_10['sentences']:
            if audio_prompt == 'mimic3':
                style_vec = msinference.compute_style(
                    synthetic_wav_paths[ix % 134])
            elif audio_prompt == 'mimic3_speed':
                style_vec = msinference.compute_style(
                    synthetic_wav_paths_AFFECT[ix % 134])
            elif audio_prompt == 'human':
                style_vec = msinference.compute_style(
                    natural_wav_paths[ix % len(natural_wav_paths)])
            else:
                print('unknonw list of style vecto')
            print(ix, text)
            ix += 1
            x = msinference.inference(text,
                                        style_vec,
                                        alpha=0.3,
                                        beta=0.7,
                                        diffusion_steps=7,
                                        embedding_scale=1)
            
            total_audio.append(x)
            # concat before write
        # -- for 10x sentenctes
        print('_____________________')
    # -- for 77x lists
    total_audio = np.concatenate(total_audio)    
    soundfile.write(f'{audio_prompt}_770.wav', total_audio, 24000)
    print(f'{audio_prompt}_full_770.wav')