Delete app.py

app.py

@@ -1,550 +0,0 @@
-import sys
-import io, os, stat
-import subprocess
-import random
-from zipfile import ZipFile
-import uuid 
-
-import time
-import torch
-import torchaudio
-# 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 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-streaming"
-
-# 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 V1.1")
-from TTS.utils.manage import ModelManager
-model_name = "tts_models/multilingual/multi-dataset/xtts_v1.1"
-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()
-
-# it should be there just to be sure
-if "ja" not in config.languages:
-    config.languages.append("ja")
-    
-# This is for debugging purposes only
-DEVICE_ASSERT_DETECTED=0
-DEVICE_ASSERT_PROMPT=None
-DEVICE_ASSERT_LANG=None
-
-#supported_languages=["pt"]
-supported_languages=config.languages
-
-def predict(prompt, language, audio_file_pth, mic_file_path, use_mic, voice_cleanup, no_lang_auto_detect, agree,):
-    if agree == True:
-        
-
-        if language not in supported_languages:
-            gr.Warning("Language you put in is not in is not in our Supported Languages, please choose from dropdown")
-                
-            return (
-                    None,
-                    None,
-                    None,
-                ) 
-
-        language_predicted=langid.classify(prompt)[0].strip() # strip need as there is space at end!
-
-        # tts expects 
-        if language_predicted 
-            #we use zh-cn 
-            language_predicted
-        print(f"Detected language:{language_predicted}n 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,
-                    ) 
-
-        
-        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,
-                ) 
-                
-        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)<1:
-            gr.Warning("Please give a longer prompt text")
-            return (
-                    None,
-                    None,
-                    None,
-                )
-        if len(prompt)>3000:
-            gr.Warning("Text length limited to characters for this demo, please try shorter text. You can clone this space and edit code for your own usage")
-            return (
-                    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}")
-
-
-        metrics_text= ""
-        
-                  
-        try:
-            
-            t_latent=time.time()
-            try:
-                gpt_cond_latent, _, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav)
-            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"
-            
-            wav_chunks = []
-    
-            t_inference=time.time()
-            
-            chunks = model.inference_stream(
-                prompt,
-                language,
-                gpt_cond_latent,
-                speaker_embedding,)  
-            
-            first_chunk=True
-            for i, chunk in enumerate(chunks):
-                if first_chunk:
-                    first_chunk_time = time.time() - t_inference
-                    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]}")
-                
-                out_file = f'{i}.wav'
-                write(out_file, 24000, chunk.detach().cpu().numpy().squeeze())
-                audio = AudioSegment.from_file(out_file)
-                audio.export(out_file, format='wav')
-                
-                yield (None, out_file, metrics_text, None)
-            
-        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+"_xtts-stream_" + 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_xtts-stream_"+ 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 
-                api.restart_space(repo_id=repo_id)
-            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,
-                    ) 
-                
-        
-        wav = torch.cat(wav_chunks, dim=0)
-        torchaudio.save("output.wav", wav.squeeze().unsqueeze(0).cpu(), 24000)
-        
-        second_of_silence = AudioSegment.silent() # use default
-        second_of_silence.export("sil.wav", format='wav')
-        
-        yield (
-            gr.make_waveform(
-                audio="output.wav",
-            ),
-            "sil.wav",
-            metrics_text,
-            speaker_wav,
-        )                
-    else:
-        gr.Warning("Please accept the Terms & Condition!")
-        return (
-                None,
-                None,
-                None,
-                None,
-            ) 
-
-
-title = "Coqui🐸 XTTS - Streaming"
-
-description = """
-<div>
-<a style="display:inline-block" href='https://github.com/coqui-ai/TTS'><img src='https://img.shields.io/github/stars/coqui-ai/TTS?style=social' /></a>
-<a style='display:inline-block' href='https://discord.gg/5eXr5seRrv'><img src='https://discord.com/api/guilds/1037326658807533628/widget.png?style=shield' /></a>
-<a href="https://huggingface.co/spaces/coqui/xtts-streaming?duplicate=true">
-<img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-</div>
-<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=0d00920c-8cc9-4bf3-90f2-a615797e5f59" />
-<a href="https://huggingface.co/coqui/XTTS-v1">XTTS</a> is a Voice generation model that lets you clone voices into different languages by using just a quick 6-second audio clip. 
-<br/>
-XTTS is built on previous research, like Tortoise, with additional architectural innovations and training to make cross-language voice cloning and multilingual speech generation possible. 
-<br/>
-This is the same model that powers our creator application <a href="https://coqui.ai">Coqui Studio</a> as well as the <a href="https://docs.coqui.ai">Coqui API</a>. In production we apply modifications to make low-latency streaming possible.
-<br/>
-Leave a star on the Github <a href="https://github.com/coqui-ai/TTS">🐸TTS</a>, where our open-source inference and training code lives.
-<br/>
-<p>For faster inference without waiting in the queue, you should duplicate this space and upgrade to GPU via the settings.
-<br/>
-
-</p>
-<p>Language Selectors: 
-Arabic: ar, Brazilian Portuguese: pt 
-</p>
-<p> Notice: Autoplay may not work on mobile, if you see black waveform image on mobile click it your Audio is there</p>
-<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=8946ef36-c454-4a8e-a9c9-8a8dd735fabd" />
-"""
-
-article = """
-<div style='margin:20px auto;'>
-<p>By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml</p>
-<p>We collect data only for error cases for improvement.</p>
-</div>
-"""
-examples = [
-    [ 
-        "Once when I was six years old I saw a magnificent picture",
-        "en",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Lorsque j'avais six ans j'ai vu, une fois, une magnifique image",
-        "fr",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Als ich sechs war, sah ich einmal ein wunderbares Bild",
-        "de",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Cuando tenía seis años, vi una vez una imagen magnífica",
-        "es",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Quando eu tinha seis anos eu vi, uma vez, uma imagem magnífica",
-        "pt",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Kiedy miałem sześć lat, zobaczyłem pewnego razu wspaniały obrazek",
-        "pl",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Un tempo lontano, quando avevo sei anni, vidi un magnifico disegno",
-        "it",
-        "examples/female.wav",
-        None,
-       False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Bir zamanlar, altı yaşındayken, muhteşem bir resim gördüm",
-        "tr",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Когда мне было шесть лет, я увидел однажды удивительную картинку",
-        "ru",
-        "examples/female.wav",
-        None,
-       False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Toen ik een jaar of zes was, zag ik op een keer een prachtige plaat",
-        "nl",
-        "examples/male.wav",
-        None,
-       False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Když mi bylo šest let, viděl jsem jednou nádherný obrázek",
-        "cs",
-        "examples/female.wav",
-        None,
-       False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "当我还只有六岁的时候， 看到了一副精彩的插画",
-        "zh-cn",
-        "examples/female.wav",
-        None,
-       False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "かつて 六歳のとき、素晴らしい絵を見ました",
-        "ja",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    
-]
-
-
-
-gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Textbox(
-            label="Text Prompt",
-            info="One or two sentences at a time is better. Up to  text characters.",
-            value="Hi there, I'm your new voice clone. Try your best to upload quality audio",
-        ),
-        gr.Dropdown(
-            label="Language",
-            info="Select an output language for the synthesised speech",
-            choices=[
-                
-                "pt",
-
-            ],
-            max_choices=1,
-            value="pt",
-        ),
-        gr.Audio(
-            label="Reference Audio",
-            info="Click on the ✎ button to upload your own target speaker audio",
-            type="filepath",
-            value="examples/female.wav",
-        ),
-        gr.Audio(source="microphone",
-                 type="filepath",
-                 info="Use your microphone to record audio",
-                 label="Use Microphone for Reference"),
-        gr.Checkbox(label="Use Microphone",
-                    value=False,
-                    info="Notice: Microphone input may not work properly under traffic",),
-        gr.Checkbox(label="Cleanup Reference Voice",
-                    value=False,
-                    info="This check can improve output if your microphone or reference voice is noisy",
-                    ),
-        gr.Checkbox(label="Do not use language auto-detect",
-                    value=False,
-                    info="Check to disable language auto-detection",),
-        gr.Checkbox(
-            label="Agree",
-            value=False,
-            info="I agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml",
-        ),
-
-        
-    ],
-    outputs=[
-        gr.Video(label="Waveform Visual"),
-        gr.Audio(label="Synthesised Audio", streaming=True, autoplay=True),
-        gr.Text(label="Metrics"),
-        gr.Audio(label="Reference Audio Used"),
-    ],
-    title=title,
-    description=description,
-    article=article,
-    examples=examples,
-    cache_examples=False,
-).queue().launch(debug=True,show_api=True)