app.py

import sys
import io, os, stat
import subprocess
import random
from zipfile import ZipFile
import uuid
import time
import torch
import torchaudio
import numpy as np
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from transformers import pipeline as PIPELINE



#download for mecab
os.system('python -m unidic download')

# By using XTTS you agree to CPML license https://coqui.ai/cpml
os.environ["COQUI_TOS_AGREED"] = "1"

# langid is used to detect language for longer text
# Most users expect text to be their own language, there is checkbox to disable it
import langid
import base64
import csv
from io import StringIO
import datetime
import re

import gradio as gr
from scipy.io.wavfile import write
from pydub import AudioSegment

from TTS.api import TTS
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts
from TTS.utils.generic_utils import get_user_data_dir

# This will trigger downloading model
print("Downloading if not downloaded Coqui XTTS V2")
from TTS.utils.manage import ModelManager

model_name = "tts_models/multilingual/multi-dataset/xtts_v2"
ModelManager().download_model(model_name)
model_path = os.path.join(get_user_data_dir("tts"), model_name.replace("/", "--"))
print("XTTS downloaded")

config = XttsConfig()
config.load_json(os.path.join(model_path, "config.json"))
model = Xtts.init_from_config(config)

def show_error(message):
    root = tk.Tk()
    root.withdraw()
    messagebox.showerror("Error", f"Has your computer been sleeping for a while? Error occured. Please reboot and re-open the application. \n Error: {e}")

try:
    model.load_checkpoint(
    config,
    checkpoint_path=os.path.join(model_path, "model.pth"),
    vocab_path=os.path.join(model_path, "vocab.json"),
    eval=True,
    use_deepspeed=True,
)
except RuntimeError as e:
    if "CUDA unknown error" in str(e):
        show_error(str(e))
model.cuda()
device = "cuda"
device2 = "cpu"

whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-medium")
print("Processor Loaded")
whisper = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium").to(device2)
print("Whisper Loaded")
whisper.config.forced_decoder_ids = None
print("Whisper Configs Set")

# This is for debugging purposes only
DEVICE_ASSERT_DETECTED = 0
DEVICE_ASSERT_PROMPT = None
DEVICE_ASSERT_LANG = None

supported_languages = config.languages

def predict(
    prompt,
    language,
    audio_file_pth,
    mic_file_path,
    use_mic,
    voice_cleanup,
    no_lang_auto_detect,
):

    if language not in supported_languages:
        gr.Warning(
            f"Language you put {language} in is not in is not in our Supported Languages, please choose from dropdown"
        )

        return (
            None,
            None,
            None,
            None,
        )

    language_predicted = langid.classify(prompt)[
        0
    ].strip()  # strip need as there is space at end!

    # tts expects chinese as zh-cn
    if language_predicted == "zh":
        # we use zh-cn
        language_predicted = "zh-cn"

    print(f"Detected language:{language_predicted}, Chosen language:{language}")

    # After text character length 15 trigger language detection
    if len(prompt) > 15:
        # allow any language for short text as some may be common
        # If user unchecks language autodetection it will not trigger
        # You may remove this completely for own use
        if language_predicted != language and not no_lang_auto_detect:
            # Please duplicate and remove this check if you really want this
            # Or auto-detector fails to identify language (which it can on pretty short text or mixed text)
            gr.Warning(
                f"It looks like your text isn’t the language you chose , if you’re sure the text is the same language you chose, please check disable language auto-detection checkbox"
            )

            return (
                None,
                None,
                None,
                None,
            )

    if use_mic == True:
        if mic_file_path is not None:
            speaker_wav = mic_file_path
        else:
            gr.Warning(
                "Please record your voice with Microphone, or uncheck Use Microphone to use reference audios"
            )
            return (
                None,
                None,
                None,
                None,
            )

    else:
        speaker_wav = audio_file_pth

    # Filtering for microphone input, as it has BG noise, maybe silence in beginning and end
    # This is fast filtering not perfect

    # Apply all on demand
    lowpassfilter = denoise = trim = loudness = True

    if lowpassfilter:
        lowpass_highpass = "lowpass=8000,highpass=75,"
    else:
        lowpass_highpass = ""

    if trim:
        # better to remove silence in beginning and end for microphone
        trim_silence = "areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,"
    else:
        trim_silence = ""

    if voice_cleanup:
        try:
            out_filename = (
                speaker_wav + str(uuid.uuid4()) + ".wav"
            )  # ffmpeg to know output format

            # we will use newer ffmpeg as that has afftn denoise filter
            shell_command = f"./ffmpeg -y -i {speaker_wav} -af {lowpass_highpass}{trim_silence} {out_filename}".split(
                " "
            )

            command_result = subprocess.run(
                [item for item in shell_command],
                capture_output=False,
                text=True,
                check=True,
            )
            speaker_wav = out_filename
            print("Filtered microphone input")
        except subprocess.CalledProcessError:
            # There was an error - command exited with non-zero code
            print("Error: failed filtering, use original microphone input")
    else:
        speaker_wav = speaker_wav

    if len(prompt) < 2:
        gr.Warning("Please give a longer prompt text")
        return (
            None,
            None,
            None,
            None,
        )

    global DEVICE_ASSERT_DETECTED
    if DEVICE_ASSERT_DETECTED:
        global DEVICE_ASSERT_PROMPT
        global DEVICE_ASSERT_LANG
        # It will likely never come here as we restart space on first unrecoverable error now
        print(
            f"Unrecoverable exception caused by language:{DEVICE_ASSERT_LANG} prompt:{DEVICE_ASSERT_PROMPT}"
        )
    try:
        metrics_text = ""
        t_latent = time.time()

        # note diffusion_conditioning not used on hifigan (default mode), it will be empty but need to pass it to model.inference
        try:
            (
                gpt_cond_latent,
                speaker_embedding,
            ) = model.get_conditioning_latents(audio_path=speaker_wav, gpt_cond_len=30, gpt_cond_chunk_len=4, max_ref_length=60)
        except Exception as e:
            print("Speaker encoding error", str(e))
            gr.Warning(
                "It appears something wrong with reference, did you unmute your microphone?"
            )
            return (
                None,
                None,
                None,
                None,
            )

        latent_calculation_time = time.time() - t_latent
        # metrics_text=f"Embedding calculation time: {latent_calculation_time:.2f} seconds\n"

        # temporary comma fix
        prompt= re.sub("([^\x00-\x7F]|\w)(\.|\。|\?)",r"\1 \2\2",prompt)

        wav_chunks = []
        ## Direct mode
        
        print("I: Generating new audio...")
        t0 = time.time()
        out = model.inference(
            prompt,
            language,
            gpt_cond_latent,
            speaker_embedding,
            repetition_penalty=5.0,
            temperature=0.75,
            enable_text_splitting=True,
        )
        inference_time = time.time() - t0
        print(f"I: Time to generate audio: {round(inference_time*1000)} milliseconds")
        metrics_text+=f"Time to generate audio: {round(inference_time*1000)} milliseconds\n"
        real_time_factor= (time.time() - t0) / out['wav'].shape[-1] * 24000
        print(f"Real-time factor (RTF): {real_time_factor}")
        metrics_text+=f"Real-time factor (RTF): {real_time_factor:.2f}\n"
        torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)


        """
        print("I: Generating new audio in streaming mode...")
        t0 = time.time()
        chunks = model.inference_stream(
            prompt,
            language,
            gpt_cond_latent,
            speaker_embedding,
            repetition_penalty=7.0,
            temperature=0.85,
        )

        first_chunk = True
        for i, chunk in enumerate(chunks):
            if first_chunk:
                first_chunk_time = time.time() - t0
                metrics_text += f"Latency to first audio chunk: {round(first_chunk_time*1000)} milliseconds\n"
                first_chunk = False
            wav_chunks.append(chunk)
            print(f"Received chunk {i} of audio length {chunk.shape[-1]}")
        inference_time = time.time() - t0
        print(
            f"I: Time to generate audio: {round(inference_time*1000)} milliseconds"
        )
        #metrics_text += (
        #    f"Time to generate audio: {round(inference_time*1000)} milliseconds\n"
        #)

        wav = torch.cat(wav_chunks, dim=0)
        print(wav.shape)
        real_time_factor = (time.time() - t0) / wav.shape[0] * 24000
        print(f"Real-time factor (RTF): {real_time_factor}")
        metrics_text += f"Real-time factor (RTF): {real_time_factor:.2f}\n"

        torchaudio.save("output.wav", wav.squeeze().unsqueeze(0).cpu(), 24000)
        """

    except RuntimeError as e:
        if "device-side assert" in str(e):
            # cannot do anything on cuda device side error, need tor estart
            print(
                f"Exit due to: Unrecoverable exception caused by language:{language} prompt:{prompt}",
                flush=True,
            )
            gr.Warning("Unhandled Exception encounter, please retry in a minute")
            print("Cuda device-assert Runtime encountered need restart")
            if not DEVICE_ASSERT_DETECTED:
                DEVICE_ASSERT_DETECTED = 1
                DEVICE_ASSERT_PROMPT = prompt
                DEVICE_ASSERT_LANG = language

            # just before restarting save what caused the issue so we can handle it in future
            # Uploading Error data only happens for unrecovarable error
            error_time = datetime.datetime.now().strftime("%d-%m-%Y-%H:%M:%S")
            error_data = [
                error_time,
                prompt,
                language,
                audio_file_pth,
                mic_file_path,
                use_mic,
                voice_cleanup,
                no_lang_auto_detect,
                agree,
            ]
            error_data = [str(e) if type(e) != str else e for e in error_data]
            print(error_data)
            print(speaker_wav)
            write_io = StringIO()
            csv.writer(write_io).writerows([error_data])
            csv_upload = write_io.getvalue().encode()

            filename = error_time + "_" + str(uuid.uuid4()) + ".csv"
            print("Writing error csv")
            error_api = HfApi()
            error_api.upload_file(
                path_or_fileobj=csv_upload,
                path_in_repo=filename,
                repo_id="coqui/xtts-flagged-dataset",
                repo_type="dataset",
            )

            # speaker_wav
            print("Writing error reference audio")
            speaker_filename = (
                error_time + "_reference_" + str(uuid.uuid4()) + ".wav"
            )
            error_api = HfApi()
            error_api.upload_file(
                path_or_fileobj=speaker_wav,
                path_in_repo=speaker_filename,
                repo_id="coqui/xtts-flagged-dataset",
                repo_type="dataset",
            )
        
        else:
            if "Failed to decode" in str(e):
                print("Speaker encoding error", str(e))
                gr.Warning(
                    "It appears something wrong with reference, did you unmute your microphone?"
                )
            else:
                print("RuntimeError: non device-side assert error:", str(e))
                gr.Warning("Something unexpected happened please retry again.")
            return (
                None,
                None,
                None,
                None,
            )
    return (
        gr.make_waveform(
            audio="output.wav",
        ),
        "output.wav",
        metrics_text,
        speaker_wav,
    )

@torch.no_grad()
def translate_from_audio(audio_prompt, record_audio_prompt, language_to_translate):
    if audio_prompt is None and record_audio_prompt is None:
        audio_prompts = torch.zeros([1, 0, NUM_QUANTIZERS]).type(torch.int32).to(device)
        text_prompts = torch.zeros([1, 0]).type(torch.int32)
        lang_pr = 'en'
        text_pr = ""
        enroll_x_lens = 0
        wav_pr, sr = None, None
    else:
        audio_prompts=None
        audio_prompt = audio_prompt if audio_prompt is not None else record_audio_prompt
        sr, wav_pr = audio_prompt
        if not isinstance(wav_pr, torch.FloatTensor):
            wav_pr = torch.FloatTensor(wav_pr)
        if wav_pr.abs().max() > 1:
            wav_pr /= wav_pr.abs().max()
        if wav_pr.size(-1) == 2:
            wav_pr = wav_pr[:, 0]
        if wav_pr.ndim == 1:
            wav_pr = wav_pr.unsqueeze(0)
        assert wav_pr.ndim and wav_pr.size(0) == 1
        
        lang_pr, text_pr = transcribe(wav_pr, sr, language_to_translate)

    message = f"Translated Text: {text_pr}"
    # delete all variables
    del wav_pr, sr, audio_prompt, record_audio_prompt
    return message

def pipe(audio):
    try:
        pipeline = PIPELINE(
            "automatic-speech-recognition",
            model="openai/whisper-small",
            chunk_length_s=30,
            device=0)
    except:
        pipeline = PIPELINE(
            "automatic-speech-recognition",
            model="openai/whisper-small",
            chunk_length_s=30,
            device=None)
    sr, wav_pr = audio 


    if wav_pr.shape[1] == 2:
        wav_pr = np.mean(wav_pr, axis=1)

    wav_pr = wav_pr.astype(np.float32)
    wav_pr /= np.max(np.abs(wav_pr))

    return pipeline({"sampling_rate": sr, "raw": wav_pr})["text"]

def transcribe(wav, sr, language_to_translate):
    if sr != 16000:
        wav4trans = torchaudio.transforms.Resample(sr, 16000)(wav)
    else:
        wav4trans = wav

    input_features = whisper_processor(wav4trans.squeeze(0), sampling_rate=16000, return_tensors="pt").input_features
    forced_decoder_ids = whisper_processor.get_decoder_prompt_ids(language=language_to_translate, task="translate")
    # generate token ids
    predicted_ids = whisper.generate(input_features.to(device2), forced_decoder_ids=forced_decoder_ids)
    lang = whisper_processor.batch_decode(predicted_ids[:, 1])[0].strip("<|>")
    # decode token ids to text
    text_pr = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]

    # print the recognized text
    print(text_pr)

    if text_pr.strip(" ")[-1] not in "?!.,。，？！。、":
        text_pr += "."

    # delete all variables
    del wav4trans, input_features, predicted_ids
    return lang, text_pr



with gr.Blocks(analytics_enabled=False) as demo:
    with gr.Tab("Translate Audio"):
        with gr.Row():
            with gr.Column():
                language_dropdown = gr.Dropdown(choices=["english", "chinese", "german", "spanish", "russian", "korean", "french", "japanese", 
                "portuguese", "turkish", "polish", "catalan", "dutch", "arabic", "swedish", "italian", "indonesian", "hindi", "finnish", "vietnamese", 
                "hebrew", "ukrainian", "greek", "malay", "czech", "romanian", "danish", "hungarian", "tamil", "norwegian", "thai", "urdu", "croatian", "bulgarian", 
                "lithuanian", "latin", "maori", "malayalam", "welsh", "slovak", "telugu", "persian", "latvian", "bengali", "serbian", "azerbaijani", "slovenian", "kannada", "estonian", 
                "macedonian", "breton", "basque", "icelandic", "armenian", "nepali", "mongolian", "bosnian", "kazakh", "albanian", "swahili", "galician", "marathi", "punjabi", "sinhala", 
                "khmer", "shona", "yoruba", "somali", "afrikaans", "occitan", "georgian", "belarusian", "tajik", "sindhi", "gujarati", "amharic", "yiddish", "lao", "uzbek", 
                "faroese", "haitian creole", "pashto", "turkmen", "nynorsk", "maltese", "sanskrit", "luxembourgish", "myanmar", "tibetan", "tagalog", "malagasy", "assamese", 
                "tatar", "hawaiian", "lingala", "hausa", "bashkir", "javanese", "sundanese", "cantonese"], value='english',
                                                label='language')
                upload_audio_prompt = gr.Audio(label='uploaded audio file', sources='upload', interactive=True)
                record_audio_prompt = gr.Audio(label='recorded audio file', sources='microphone', interactive=True)
                
            with gr.Column():
                text_output_translate = gr.Textbox(label="Message")
                btn_translate = gr.Button("Translate!")
                btn_translate.click(translate_from_audio,
                            inputs = [upload_audio_prompt, record_audio_prompt, language_dropdown],
                            outputs = [text_output_translate])

    with gr.Tab("Transcribe Audio"):
        with gr.Row():
            with gr.Column():
                upload_audio_prompt_transcript = gr.Audio(label = 'uploaded audio file', sources = 'upload', interactive = True)
                record_audio_prompt_transcript = gr.Audio(label= 'recorded audio file', sources = "microphone", interactive = True)
            with gr.Column():
                text_output_transcript = gr.Textbox(label="Transcription")
                btn_transcribe = gr.Button("Transcribe!")
            if upload_audio_prompt_transcript!=None:
                btn_transcribe.click(pipe,
                        inputs = [upload_audio_prompt_transcript],
                        outputs = [text_output_transcript])
            elif record_audio_prompt_transcript!=None:
                btn_transcribe.click(pipe,
                        inputs = [record_audio_prompt_transcript],
                        outputs = [text_output_transcript])

    with gr.Tab("Voice Cloning"):    
        with gr.Row():
            with gr.Column():
                input_text_gr = gr.Textbox(
                    label="Text Prompt",
                    info="One or two sentences at a time is better. Up to 200 text characters.",
                    value="Hi there, I'm your new voice clone. Try your best to upload quality audio.",
                )
                language_gr = gr.Dropdown(
                    label="Language",
                    info="Select an output language for the synthesised speech",
                    choices=[
                        "en",
                        "es",
                        "fr",
                        "de",
                        "it",
                        "pt",
                        "pl",
                        "tr",
                        "ru",
                        "nl",
                        "cs",
                        "ar",
                        "zh-cn",
                        "ja",
                        "ko",
                        "hu",
                        "hi"
                    ],
                    max_choices=1,
                    value="en",
                )
                ref_gr = gr.Audio(
                    label="Reference Audio",
                    type="filepath",
                )
                mic_gr = gr.Audio(
                    sources="microphone",
                    type="filepath",
                    label="Use Microphone for Reference",
                )
                use_mic_gr = gr.Checkbox(
                    label="Use Microphone",
                    value=False,
                )
                clean_ref_gr = gr.Checkbox(
                    label="Cleanup Reference Voice",
                    value=False,
                )
                auto_det_lang_gr = gr.Checkbox(
                    label="Do not use language auto-detect",
                    value=False,
                )

                tts_button = gr.Button("Send", elem_id="send-btn", visible=True)


            with gr.Column():
                video_gr = gr.Video(label="Waveform Visual")
                audio_gr = gr.Audio(label="Synthesised Audio", autoplay=True)
                out_text_gr = gr.Text(label="Metrics")
                ref_audio_gr = gr.Audio(label="Reference Audio Used")


        tts_button.click(predict, [input_text_gr, language_gr, ref_gr, mic_gr, use_mic_gr, clean_ref_gr, auto_det_lang_gr], outputs=[video_gr, audio_gr, out_text_gr, ref_audio_gr])

demo.queue()  
demo.launch(debug=True, show_api=True, share=True, server_name='0.0.0.0')