app_srt.py

import sys
import io, os, stat
import subprocess
import random
from zipfile import ZipFile
import uuid
import time
import torch
import torchaudio
import gradio as gr

#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

HF_TOKEN = os.environ.get("HF_TOKEN")

from huggingface_hub import HfApi

# will use api to restart space on a unrecoverable error
api = HfApi(token=HF_TOKEN)
repo_id = "coqui/xtts"

# Use never ffmpeg binary for Ubuntu20 to use denoising for microphone input
print("Export newer ffmpeg binary for denoise filter")
ZipFile("ffmpeg.zip").extractall()
print("Make ffmpeg binary executable")
st = os.stat("ffmpeg")
os.chmod("ffmpeg", st.st_mode | stat.S_IEXEC)

# 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)
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,
)
model.cuda()

# 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,
    save_path
):
    voice_cleanup = False
    mic_file_path = None
    use_mic = False
    agree = True
    no_lang_auto_detect = False
    if agree == True:
        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,
            )
        if len(prompt) > 500:
            gr.Warning(
                "Text length limited to 500 characters for this demo, please try shorter text. You can clone this space and edit code for your own usage"
            )
            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}"
            )

            # HF Space specific.. This error is unrecoverable need to restart space
            space = api.get_space_runtime(repo_id=repo_id)
            if space.stage!="BUILDING":
                api.restart_space(repo_id=repo_id)
            else:
                print("TRIED TO RESTART but space is building")

        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,
            )
            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(f"output/{save_path}.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",
                )

                # HF Space specific.. This error is unrecoverable need to restart space
                space = api.get_space_runtime(repo_id=repo_id)
                if space.stage!="BUILDING":
                    api.restart_space(repo_id=repo_id)
                else:
                    print("TRIED TO RESTART but space is building")

            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 (
            f"output/{save_path}.wav"
        )
    else:
        gr.Warning("Please accept the Terms & Condition!")
        return (
            None
        )

class subtitle:
    def __init__(self,index:int, start_time, end_time, text:str):
        self.index = int(index)
        self.start_time = start_time
        self.end_time = end_time
        self.text = text.strip()
    def normalize(self,ntype:str,fps=30):
         if ntype=="prcsv":
              h,m,s,fs=(self.start_time.replace(';',':')).split(":")#seconds
              self.start_time=int(h)*3600+int(m)*60+int(s)+round(int(fs)/fps,2)
              h,m,s,fs=(self.end_time.replace(';',':')).split(":")
              self.end_time=int(h)*3600+int(m)*60+int(s)+round(int(fs)/fps,2)
         elif ntype=="srt":
             h,m,s=self.start_time.split(":")
             s=s.replace(",",".")
             self.start_time=int(h)*3600+int(m)*60+round(float(s),2)
             h,m,s=self.end_time.split(":")
             s=s.replace(",",".")
             self.end_time=int(h)*3600+int(m)*60+round(float(s),2)
         else:
             raise ValueError
    def add_offset(self,offset=0):
        self.start_time+=offset
        if self.start_time<0:
            self.start_time=0
        self.end_time+=offset
        if self.end_time<0:
            self.end_time=0
    def __str__(self) -> str:
        return f'id:{self.index},start:{self.start_time},end:{self.end_time},text:{self.text}'

def read_srt(uploaded_file):
    offset=0
    with open(uploaded_file.name,"r",encoding="utf-8") as f:
        file=f.readlines()
    subtitle_list=[]
    indexlist=[]
    filelength=len(file)
    for i in range(0,filelength):
        if " --> " in file[i]:
            is_st=True
            for char in file[i-1].strip().replace("\ufeff",""):
                if char not in ['0','1','2','3','4','5','6','7','8','9']:
                    is_st=False
                    break
            if is_st:
                indexlist.append(i) #get line id
    listlength=len(indexlist)
    for i in range(0,listlength-1):
        st,et=file[indexlist[i]].split(" --> ")
        id=int(file[indexlist[i]-1].strip().replace("\ufeff",""))
        text=""
        for x in range(indexlist[i]+1,indexlist[i+1]-2):
            text+=file[x]
        st=subtitle(id,st,et,text)
        st.normalize(ntype="srt")
        st.add_offset(offset=offset)
        subtitle_list.append(st)
    st,et=file[indexlist[-1]].split(" --> ")
    id=file[indexlist[-1]-1]
    text=""
    for x in range(indexlist[-1]+1,filelength):
        text+=file[x]
    st=subtitle(id,st,et,text)
    st.normalize(ntype="srt")
    st.add_offset(offset=offset)
    subtitle_list.append(st)
    return subtitle_list

from pydub import AudioSegment

def trim_audio(intervals, input_file_path, output_file_path):
    # load the audio file
    audio = AudioSegment.from_file(input_file_path)

    # iterate over the list of time intervals
    for i, (start_time, end_time) in enumerate(intervals):
        # extract the segment of the audio
        segment = audio[start_time*1000:end_time*1000]

        # construct the output file path
        output_file_path_i = f"{output_file_path}_{i}.wav"

        # export the segment to a file
        segment.export(output_file_path_i, format='wav')

import re

def merge_audios(input_dir):
    output_file = "AI配音版.wav"
    # List all .wav files in the directory
    files = [f for f in os.listdir(input_dir) if f.endswith('.wav')]

    # Sort files based on the numerical order extracted from their names
    sorted_files = sorted(files, key=lambda x: int(re.search(r'(\d+)', x).group()))

    # Initialize an empty audio segment
    combined = AudioSegment.empty()

    # Loop through the sorted list and concatenate them
    for file in sorted_files:
        path = os.path.join(input_dir, file)
        audio = AudioSegment.from_wav(path)
        combined += audio
        print(f"Merged: {file}")

    # Export the combined audio
    combined.export(output_file, format="wav")
    return "AI配音版.wav"
    
def convert_from_srt(filename, audio_full, language):
    subtitle_list = read_srt(filename)
    for i in subtitle_list:
        os.makedirs("output", exist_ok=True)
        print(f"正在合成第{i.index}条语音")
        print(f"语音内容：{i.text}")
        trim_audio([[i.start_time, i.end_time]], audio_full, f"sliced_audio_{i.index}")
        predict(i.text, language, f"sliced_audio_{i.index}_0.wav", i.text + " " + str(i.index))
    return merge_audios("output")

with gr.Blocks() as app:
    gr.Markdown("# <center>🌊💕🎶 XTTS - SRT文件一键AI配音</center>")
    gr.Markdown("### <center>🌟 只需上传SRT文件和原版配音文件即可，每次一集视频AI自动配音！Developed by Kevin Wang </center>")
    with gr.Row():
        with gr.Column():
            inp1 = gr.File(file_count="single", label="请上传一集视频对应的SRT文件")
            inp2 = gr.Audio(label="请上传一集视频对应的原声配音", info="需要是.wav音频文件", type="filepath")
            inp3 = gr.Dropdown(
                label="请选择SRT文件对应的语言",
                info="各种语言的简写代码请参考[此网站](https://www.science.co.il/language/Codes.php)",
                choices=[
                    "en",
                    "es",
                    "fr",
                    "de",
                    "it",
                    "pt",
                    "pl",
                    "tr",
                    "ru",
                    "nl",
                    "cs",
                    "ar",
                    "zh-cn",
                    "ja",
                    "ko",
                    "hu",
                    "hi"
                ],
                max_choices=1,
                value="en",
            )
            btn = gr.Button("一键开启AI配音吧💕", variant="primary")
        with gr.Column():
            out1 = gr.Audio(label="为您生成的AI完整配音")

        btn.click(convert_from_srt, [inp1, inp2, inp3], [out1])
        
    gr.Markdown("### <center>注意❗：请不要生成会对任何个人或组织造成侵害的内容。</center>")
    gr.HTML('''
        <div class="footer">
                    <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘
                    </p>
        </div>
    ''')

app.launch(share=True, show_error=True)