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.gitattributes

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+examples/female.wav filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: XTTS-streaming
+emoji: 🐸
+colorFrom: green
+colorTo: red
+sdk: static
+pinned: false
+models:
+- coqui/XTTS-v1
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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 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,
+                    ) 
+
+        
+        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="en",
+        ),
+        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)

examples/female.wav

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+version https://git-lfs.github.com/spec/v1
+oid sha256:89a4fa9a16b6463f852cf9424f72c3d3c87aa83010e89db534c53fcd1ae12c02
+size 1002030