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| """ | |
| TODO: | |
| + [x] Load Configuration | |
| + [ ] Checking | |
| + [ ] Better saving directory | |
| """ | |
| import numpy as np | |
| from pathlib import Path | |
| import torch.nn as nn | |
| import torch | |
| import torchaudio | |
| from transformers import pipeline | |
| from pathlib import Path | |
| import datetime | |
| import pdb | |
| # local import | |
| import sys | |
| from espnet2.bin.tts_inference import Text2Speech | |
| from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| sys.path.append("src") | |
| import gradio as gr | |
| from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq | |
| processor = AutoProcessor.from_pretrained("KevinGeng/whipser_medium_en_PAL300_step25") | |
| model = AutoModelForSpeechSeq2Seq.from_pretrained("KevinGeng/whipser_medium_en_PAL300_step25") | |
| transcriber = pipeline("automatic-speech-recognition", model="KevinGeng/whipser_medium_en_PAL300_step25") | |
| # Text2Mel models | |
| # @title English multi-speaker pretrained model { run: "auto" } | |
| lang = "English" | |
| vits_tag = "kan-bayashi/libritts_xvector_vits" | |
| ft2_tag = "kan-bayashi/libritts_xvector_conformer_fastspeech2" | |
| transformer_tag = "kan-bayashi/libritts_xvector_transformer" | |
| # !!! vits needs no vocoder !!! | |
| # Local Text2Mel models | |
| vits_config_local = "TTS_models/libritts_xvector_vits/config.yaml" | |
| vits_model_local = "TTS_models/libritts_xvector_vits/train.total_count.ave_10best.pth" | |
| # TODO | |
| ft2_config_local = "" | |
| ft2_model_local= "" | |
| transformer_config_local = "" | |
| transformer_config_local = "" | |
| # Vocoders | |
| vocoder_tag = "parallel_wavegan/vctk_parallel_wavegan.v1.long" # @param ["none", "parallel_wavegan/vctk_parallel_wavegan.v1.long", "parallel_wavegan/vctk_multi_band_melgan.v2", "parallel_wavegan/vctk_style_melgan.v1", "parallel_wavegan/vctk_hifigan.v1", "parallel_wavegan/libritts_parallel_wavegan.v1.long", "parallel_wavegan/libritts_multi_band_melgan.v2", "parallel_wavegan/libritts_hifigan.v1", "parallel_wavegan/libritts_style_melgan.v1"] {type:"string"} | |
| hifigan_vocoder_tag = "parallel_wavegan/parallel_wavegan/libritts_hifigan.v1" # @param ["none", "parallel_wavegan/vctk_parallel_wavegan.v1.long", "parallel_wavegan/vctk_multi_band_melgan.v2", "parallel_wavegan/vctk_style_melgan.v1", "parallel_wavegan/vctk_hifigan.v1", "parallel_wavegan/libritts_parallel_wavegan.v1.long", "parallel_wavegan/libritts_multi_band_melgan.v2", "parallel_wavegan/libritts_hifigan.v1", "parallel_wavegan/libritts_style_melgan.v1"] {type:"string"} | |
| # Local Vocoders | |
| ## Make sure the use parallel_wavegan as prefix (PWG feature) | |
| vocoder_tag_local = "parallel_wavegan/vctk_parallel_wavegan.v1.long" | |
| hifigan_vocoder_tag_local = "parallel_wavegan/libritts_hifigan.v1" | |
| from espnet2.bin.tts_inference import Text2Speech | |
| from espnet2.utils.types import str_or_none | |
| # local import | |
| text2speech = Text2Speech.from_pretrained( | |
| train_config = vits_config_local, | |
| model_file=vits_model_local, | |
| device="cuda", | |
| use_att_constraint=False, | |
| backward_window=1, | |
| forward_window=3, | |
| speed_control_alpha=1.0, | |
| ) | |
| # # Fastspeech2 | |
| # ft2_text2speech = Text2Speech.from_pretrained( | |
| # model_tag=ft2_tag, | |
| # vocoder_tag=str_or_none(vocoder_tag_local), | |
| # device="cuda", | |
| # use_att_constraint=False, | |
| # backward_window=1, | |
| # forward_window=3, | |
| # speed_control_alpha=1.0, | |
| # ) | |
| # # Fastspeech2 + hifigan | |
| # ft2_text2speech_hifi = Text2Speech.from_pretrained( | |
| # model_tag=ft2_tag, | |
| # vocoder_tag=str_or_none(hifigan_vocoder_tag_local), | |
| # device="cuda", | |
| # use_att_constraint=False, | |
| # backward_window=1, | |
| # forward_window=3, | |
| # speed_control_alpha=1.0, | |
| # ) | |
| # # transformer tag | |
| # transformer_text2speech = Text2Speech.from_pretrained( | |
| # model_tag=transformer_tag, | |
| # vocoder_tag=str_or_none(vocoder_tag_local), | |
| # device="cuda", | |
| # use_att_constraint=False, | |
| # backward_window=1, | |
| # forward_window=3, | |
| # speed_control_alpha=1.0, | |
| # ) | |
| import glob | |
| import os | |
| import numpy as np | |
| import kaldiio | |
| # Get model directory path | |
| # from espnet_model_zoo.downloader import ModelDownloader | |
| # d = ModelDownloader() | |
| # model_dir = os.path.dirname(d.download_and_unpack(tag)["train_config"]) | |
| # Speaker x-vector selection | |
| xvector_ark = [ | |
| p | |
| for p in glob.glob( | |
| f"xvector/test-clean/spk_xvector.ark", recursive=True | |
| ) | |
| if "test" in p | |
| ][0] | |
| xvectors = {k: v for k, v in kaldiio.load_ark(xvector_ark)} | |
| spks = list(xvectors.keys()) | |
| male_spks = { | |
| "Male1": "260_123286", | |
| "Male2": "1320_122612", | |
| "Male3": "672_122797" | |
| } | |
| female_spks = {"Female1": "5683_32865", | |
| "Female2": "121_121726", | |
| "Female3": "8463_287645"} | |
| spks = dict(male_spks, **female_spks) | |
| spk_names = sorted(spks.keys()) | |
| def ASRTTS(audio_file, spk_name, ref_text=""): | |
| spk = spks[spk_name] | |
| spembs = xvectors[spk] | |
| if ref_text == "": | |
| reg_text = transcriber(audio_file)["text"] | |
| else: | |
| reg_text = ref_text | |
| speech, sr = torchaudio.load( | |
| audio_file, channels_first=True | |
| ) # Mono channel | |
| wav_tensor_spembs = text2speech( | |
| text=reg_text, speech=speech, spembs=spembs | |
| )["wav"] | |
| wav_numpy = wav_tensor_spembs.unsqueeze(1).to("cpu") | |
| sample_rate = 22050 | |
| save_id = ( | |
| "./wav/" + Path(audio_file).stem + "_" + spk_name + "_spkembs.wav" | |
| ) | |
| torchaudio.save( | |
| save_id, | |
| src=wav_tensor_spembs.unsqueeze(0).to("cpu"), | |
| sample_rate=22050, | |
| ) | |
| return save_id, reg_text | |
| def ASRTTS_clean(audio_file, spk_name): | |
| spk = spks[spk_name] | |
| spembs = xvectors[spk] | |
| reg_text = transcriber(audio_file)["text"] | |
| speech, sr = torchaudio.load( | |
| audio_file, channels_first=True | |
| ) # Mono channel | |
| wav_tensor_spembs = text2speech( | |
| text=reg_text, speech=speech, spembs=spembs | |
| )["wav"] | |
| wav_numpy = wav_tensor_spembs.unsqueeze(1).to("cpu") | |
| sample_rate = 22050 | |
| # create another saving id wav file with current time stamp in YYYYMMDD_HHMMSS format | |
| save_id = ( | |
| "./wav/" + str(datetime.datetime.now().strftime("%Y%m%d_%H%M%S")) + ".wav" | |
| ) | |
| torchaudio.save( | |
| save_id, | |
| src=wav_tensor_spembs.unsqueeze(0).to("cpu"), | |
| sample_rate=22050, | |
| ) | |
| return save_id | |
| def ft2_ASRTTS_clean(audio_file, spk_name): | |
| spk = spks[spk_name] | |
| spembs = xvectors[spk] | |
| reg_text = transcriber(audio_file)["text"] | |
| speech, sr = torchaudio.load( | |
| audio_file, channels_first=True | |
| ) # Mono channel | |
| wav_tensor_spembs = ft2_text2speech( | |
| text=reg_text, speech=speech, spembs=spembs | |
| )["wav"] | |
| wav_numpy = wav_tensor_spembs.unsqueeze(1).to("cpu") | |
| sample_rate = 22050 | |
| save_id = ( | |
| "./wav/" + Path(audio_file).stem + "_fs2_" + spk_name + "_spkembs.wav" | |
| ) | |
| torchaudio.save( | |
| save_id, | |
| src=wav_tensor_spembs.unsqueeze(0).to("cpu"), | |
| sample_rate=22050, | |
| ) | |
| return save_id | |
| def ft2_ASRTTS_clean_hifi(audio_file, spk_name): | |
| spk = spks[spk_name] | |
| spembs = xvectors[spk] | |
| reg_text = transcriber(audio_file)["text"] | |
| speech, sr = torchaudio.load( | |
| audio_file, channels_first=True | |
| ) # Mono channel | |
| wav_tensor_spembs = ft2_text2speech_hifi( | |
| text=reg_text, speech=speech, spembs=spembs | |
| )["wav"] | |
| wav_numpy = wav_tensor_spembs.unsqueeze(1).to("cpu") | |
| sample_rate = 22050 | |
| save_id = ( | |
| "./wav/" + Path(audio_file).stem + "_fs2_hifi_" + spk_name + "_spkembs.wav" | |
| ) | |
| torchaudio.save( | |
| save_id, | |
| src=wav_tensor_spembs.unsqueeze(0).to("cpu"), | |
| sample_rate=22050, | |
| ) | |
| return save_id | |
| def transformer_ASRTTS_clean(audio_file, spk_name): | |
| spk = spks[spk_name] | |
| spembs = xvectors[spk] | |
| reg_text = transcriber(audio_file)["text"] | |
| speech, sr = torchaudio.load( | |
| audio_file, channels_first=True | |
| ) # Mono channel | |
| wav_tensor_spembs = transformer_text2speech( | |
| text=reg_text, speech=speech, spembs=spembs | |
| )["wav"] | |
| wav_numpy = wav_tensor_spembs.unsqueeze(1).to("cpu") | |
| sample_rate = 22050 | |
| save_id = ( | |
| "./wav/" + Path(audio_file).stem + "_transformer_" + spk_name + "_spkembs.wav" | |
| ) | |
| torchaudio.save( | |
| save_id, | |
| src=wav_tensor_spembs.unsqueeze(0).to("cpu"), | |
| sample_rate=22050, | |
| ) | |
| return save_id | |
| # def google_ASRTTS_clean(audio_file, spk_name): | |
| # spk = spks[spk_name] | |
| # spembs = xvectors[spk] | |
| # reg_text = transcriber(audio_file)["text"] | |
| # # pdb.set_trace() | |
| # synthesis_input = texttospeech.SynthesisInput(text=reg_text) | |
| # voice = texttospeech.VoiceSelectionParams( | |
| # language_code="en-US", ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL | |
| # ) | |
| # audio_config = texttospeech.AudioConfig( | |
| # audio_encoding=texttospeech.AudioEncoding.MP3 | |
| # ) | |
| # response = Google_TTS_client.synthesize_speech( | |
| # input=synthesis_input, voice=voice, audio_config=audio_config | |
| # ) | |
| # save_id = ( | |
| # "./wav/" + Path(audio_file).stem + "_google_" + spk_name + "_spkembs.wav" | |
| # ) | |
| # with open(save_id, "wb") as out_file: | |
| # out_file.write(response.audio_content) | |
| # return save_id | |
| reference_textbox = gr.Textbox( | |
| value="", | |
| placeholder="Input reference here", | |
| label="Reference", | |
| ) | |
| recognization_textbox = gr.Textbox( | |
| value="", | |
| placeholder="Output recognization here", | |
| label="recognization_textbox", | |
| ) | |
| speaker_option = gr.Radio(choices=spk_names, label="Speaker") | |
| input_audio = gr.Audio( | |
| source="upload", type="filepath", label="Audio_to_Evaluate" | |
| ) | |
| output_audio = gr.Audio( | |
| source="upload", file="filepath", label="Synthesized Audio" | |
| ) | |
| examples = [ | |
| ["./samples/001.wav", "M1", ""], | |
| ["./samples/002.wav", "M2", ""], | |
| ["./samples/003.wav", "F1", ""], | |
| ["./samples/004.wav", "F2", ""], | |
| ] | |
| def change_audiobox(choice): | |
| if choice == "upload": | |
| input_audio = gr.Audio(source="upload", visible=True) | |
| elif choice == "microphone": | |
| input_audio = gr.Audio(source="microphone", visible=True) | |
| else: | |
| input_audio = gr.Audio(visible=False) | |
| return input_audio | |
| def show_icon(choice): | |
| if choice == "Male1": | |
| spk_icon = gr.Image.update(value="speaker_icons/male1.png", visible=True) | |
| elif choice == "Male2": | |
| spk_icon = gr.Image.update(value="speaker_icons/male2.png", visible=True) | |
| elif choice == "Male3": | |
| spk_icon = gr.Image.update(value="speaker_icons/male3.png", visible=True) | |
| elif choice == "Female1": | |
| spk_icon = gr.Image.update(value="speaker_icons/female1.png", visible=True) | |
| elif choice == "Female2": | |
| spk_icon = gr.Image.update(value="speaker_icons/female2.png", visible=True) | |
| elif choice == "Female3": | |
| spk_icon = gr.Image.update(value="speaker_icons/female3.png", visible=True) | |
| return spk_icon | |
| def get_download_file(audio_file=None): | |
| if audio_file == None: | |
| output_audio_file = gr.File.update(visible=False) | |
| else: | |
| output_audio_file = gr.File.update(visible=True) | |
| return output_audio_file | |
| def download_file(audio_file): | |
| return gr.File(value=audio_file) | |
| # pdb.set_trace() | |
| with gr.Blocks( | |
| analytics_enabled=False, | |
| css=".gradio-container {background-color: #78BD91}", | |
| ) as demo: | |
| # Public Version | |
| with gr.Tab("Open Version"): | |
| with gr.Column(elem_id="Column"): | |
| input_format = gr.Radio( | |
| choices=["microphone", "upload"], label="Choose your input format", elem_id="input_format" | |
| ) | |
| input_audio = gr.Audio( | |
| source="microphone", | |
| type="filepath", | |
| label="Input Audio", | |
| interactive=True, | |
| visible=False, | |
| elem_id="input_audio" | |
| ) | |
| input_format.change( | |
| fn=change_audiobox, inputs=input_format, outputs=input_audio | |
| ) | |
| speaker_option = gr.Radio(choices=spk_names, value="Male1", label="Choose your voice profile") | |
| spk_icon = gr.Image(value="speaker_icons/male1.png", | |
| type="filepath", | |
| image_mode="RGB", | |
| source="upload", | |
| shape=[50, 50], | |
| interactive=True, | |
| visible=True) | |
| speaker_option.change( | |
| fn=show_icon, inputs=speaker_option, outputs=spk_icon | |
| ) | |
| b = gr.Button("Convert") | |
| output_audio = gr.Audio( | |
| source="upload", file="filepath", label="Converted Audio", interactive=False | |
| ) | |
| b.click( | |
| ASRTTS_clean, | |
| inputs=[input_audio, speaker_option], | |
| outputs=output_audio, | |
| api_name="convert" | |
| ) | |
| # # Tab selection: | |
| # with gr.Tab("Test Version: Multi TTS model"): | |
| # with gr.Column(elem_id="Column"): | |
| # input_format = gr.Radio( | |
| # choices=["microphone", "upload"], label="Choose your input format", elem_id="input_format" | |
| # ) | |
| # input_audio = gr.Audio( | |
| # source="microphone", | |
| # type="filepath", | |
| # label="Input Audio", | |
| # interactive=True, | |
| # visible=False, | |
| # elem_id="input_audio" | |
| # ) | |
| # input_format.change( | |
| # fn=change_audiobox, inputs=input_format, outputs=input_audio | |
| # ) | |
| # speaker_option = gr.Radio(choices=spk_names, value="Male1", label="Choose your voice profile") | |
| # spk_icon = gr.Image(value="speaker_icons/male1.png", | |
| # type="filepath", | |
| # image_mode="RGB", | |
| # source="upload", | |
| # shape=[50, 50], | |
| # interactive=True, | |
| # visible=True) | |
| # speaker_option.change( | |
| # fn=show_icon, inputs=speaker_option, outputs=spk_icon | |
| # ) | |
| # with gr.Column(): | |
| # with gr.Row(): | |
| # b2 = gr.Button("Convert") | |
| # output_audio = gr.Audio( | |
| # source="upload", file="filepath", label="Converted Audio", interactive=False | |
| # ) | |
| # b2.click( | |
| # ASRTTS_clean, | |
| # inputs=[input_audio, speaker_option], | |
| # outputs=output_audio, | |
| # api_name="convert_" | |
| # ) | |
| # with gr.Row(): | |
| # # Fastspeech2 + PWG [under construction] | |
| # b_ft2 = gr.Button("Convert_fastspeech2") | |
| # output_audio_ft2= gr.Audio( | |
| # source="upload", file="filepath", label="Converted Audio", interactive=False | |
| # ) | |
| # b_ft2.click( | |
| # ft2_ASRTTS_clean, | |
| # inputs=[input_audio, speaker_option], | |
| # outputs=output_audio_ft2, | |
| # api_name="convert_ft2" | |
| # ) | |
| # with gr.Row(): | |
| # # Fastspeech2 + hifigan [under construction] | |
| # b_ft2_hifi = gr.Button("Convert_fastspeech2+HifiGAN") | |
| # output_audio_ft2_hifi= gr.Audio( | |
| # source="upload", file="filepath", label="Converted Audio", interactive=False | |
| # ) | |
| # b_ft2_hifi.click( | |
| # ft2_ASRTTS_clean_hifi, | |
| # inputs=[input_audio, speaker_option], | |
| # outputs=output_audio_ft2_hifi, | |
| # api_name="convert_ft2_hifi" | |
| # ) | |
| # with gr.Row(): | |
| # # transformer [TODO] | |
| # b_transformer = gr.Button("Convert_transformer") | |
| # output_audio_transformer= gr.Audio( | |
| # source="upload", file="filepath", label="Converted Audio", interactive=False | |
| # ) | |
| # b_transformer.click( | |
| # transformer_ASRTTS_clean, | |
| # inputs=[input_audio, speaker_option], | |
| # outputs=output_audio_transformer, | |
| # api_name="convert_trans" | |
| # ) | |
| # google tts [TODO] | |
| # b_google = gr.Button("Convert_googleTTS") | |
| # output_audio_google= gr.Audio( | |
| # source="upload", file="filepath", label="Converted Audio", interactive=False | |
| # ) | |
| # b_google.click( | |
| # google_ASRTTS_clean, | |
| # inputs=[input_audio, speaker_option], | |
| # outputs=output_audio_google, | |
| # api_name="convert" | |
| # ) | |
| demo.launch(share=False) |