Create coqui.py

TextGen/coqui.py

@@ -0,0 +1,399 @@
+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
+
+os.system("pip install gradio-client @ git+https://github.com/gradio-app/gradio@bed454c3d22cfacedc047eb3b0ba987b485ac3fd")
+os.system("pip install git+https://github.com/gradio-app/[email protected]")
+#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
+
+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
+os.system("pip install git+https://github.com/gradio-app/[email protected]")
+# 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 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,
+        )