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 #update gradio to faster streaming #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 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" # 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 numpy_to_mp3(audio_array, sampling_rate): # Normalize audio_array if it's floating-point if np.issubdtype(audio_array.dtype, np.floating): max_val = np.max(np.abs(audio_array)) audio_array = (audio_array / max_val) * 32767 # Normalize to 16-bit range audio_array = audio_array.astype(np.int16) # Create an audio segment from the numpy array audio_segment = AudioSegment( audio_array.tobytes(), frame_rate=sampling_rate, sample_width=audio_array.dtype.itemsize, channels=1 ) # Export the audio segment to MP3 bytes - use a high bitrate to maximise quality mp3_io = io.BytesIO() audio_segment.export(mp3_io, format="mp3", bitrate="320k") # Get the MP3 bytes mp3_bytes = mp3_io.getvalue() mp3_io.close() return mp3_bytes 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( f"Language you put {language} in is not in is not in our Supported Languages, please choose from dropdown" ) return ( 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, ) 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, ) 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, ) if len(prompt) > 1000: gr.Warning( "Text length limited to 200 characters for this demo, please try shorter text. You can clone this space and edit code for your own usage" ) return ( 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, ) 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("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 # Convert chunk to numpy array and return it chunk_np = chunk.cpu().numpy() print('chunk',i) yield (24000, chunk_np) 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" #) 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, ) else: gr.Warning("Please accept the Terms & Condition!") return ( None, )