diff --git a/README.md b/README.md index 9e075147fc0146eaf0e61203cc81c9824e218c65..bf227e2c5173d1f559b715ccce1cd35c1e7e416a 100644 --- a/README.md +++ b/README.md @@ -6,13 +6,13 @@ colorTo: gray sdk: gradio sdk_version: 5.41.1 app_file: app.py -pinned: false +short_description: TTS for CPU license: cc-by-nc-4.0 tags: -- age -- gender -- expression -- audio +- non-AR +- affective +- shift +- tts --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/app.py b/app.py index 5428da6f103d6cf5d564dba4389b1301743300bf..bb9155276c0f0dbb9126c92b43f8251c78be765a 100644 --- a/app.py +++ b/app.py @@ -1,22 +1,25 @@ +# -*- coding: utf-8 -*- import typing import types # fusion of forward() of Wav2Vec2 import gradio as gr import matplotlib.pyplot as plt import numpy as np -import spaces +import os import torch import torch.nn as nn from transformers import Wav2Vec2Processor from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2Model from transformers.models.wav2vec2.modeling_wav2vec2 import Wav2Vec2PreTrainedModel - import audiofile +import unicodedata +import textwrap +from tts import StyleTTS2 import audresample device = 0 if torch.cuda.is_available() else "cpu" duration = 2 # limit processing of audio -age_gender_model_name = "audeering/wav2vec2-large-robust-24-ft-age-gender" +age_gender_model_name = "audeering/wav2vec2-large-robust-6-ft-age-gender" expression_model_name = "audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim" @@ -167,7 +170,7 @@ class ExpressionModel(Wav2Vec2PreTrainedModel): # Load models from hub -age_gender_processor = Wav2Vec2Processor.from_pretrained(age_gender_model_name) + age_gender_model = AgeGenderModel.from_pretrained(age_gender_model_name) expression_processor = Wav2Vec2Processor.from_pretrained(expression_model_name) expression_model = ExpressionModel.from_pretrained(expression_model_name) @@ -206,12 +209,9 @@ def process_func(x: np.ndarray, sampling_rate: int) -> typing.Tuple[str, dict, s }, expression_file, ) + - -@spaces.GPU -def recognize(input_file: str) -> typing.Tuple[str, dict, str]: - # sampling_rate, signal = input_microphone - # signal = signal.astype(np.float32, order="C") / 32768.0 +def recognize(input_file): if input_file is None: raise gr.Error( "No audio file submitted! " @@ -227,50 +227,6 @@ def recognize(input_file: str) -> typing.Tuple[str, dict, str]: return process_func(signal, target_rate) -def plot_expression_RIGID(arousal, dominance, valence): - r"""3D pixel plot of arousal, dominance, valence.""" - # Voxels per dimension - voxels = 7 - # Create voxel grid - x, y, z = np.indices((voxels + 1, voxels + 1, voxels + 1)) - voxel = ( - (x == round(arousal * voxels)) - & (y == round(dominance * voxels)) - & (z == round(valence * voxels)) - ) - projection = ( - (x == round(arousal * voxels)) - & (y == round(dominance * voxels)) - & (z < round(valence * voxels)) - ) - colors = np.empty((voxel | projection).shape, dtype=object) - colors[voxel] = "#fcb06c" - colors[projection] = "#fed7a9" - ax = plt.figure().add_subplot(projection='3d') - ax.voxels(voxel | projection, facecolors=colors, edgecolor='k') - ax.set_xlim([0, voxels]) - ax.set_ylim([0, voxels]) - ax.set_zlim([0, voxels]) - ax.set_aspect("equal") - ax.set_xlabel("arousal", fontsize="large", labelpad=0) - ax.set_ylabel("dominance", fontsize="large", labelpad=0) - ax.set_zlabel("valence", fontsize="large", labelpad=0) - ax.set_xticks( - list(range(voxels + 1)), - labels=[0, None, None, None, None, None, None, 1], - verticalalignment="bottom", - ) - ax.set_yticks( - list(range(voxels + 1)), - labels=[0, None, None, None, None, None, None, 1], - verticalalignment="bottom", - ) - ax.set_zticks( - list(range(voxels + 1)), - labels=[0, None, None, None, None, None, None, 1], - verticalalignment="top", - ) - def explode(data): """ Expands a 3D array by creating gaps between voxels. @@ -282,6 +238,18 @@ def explode(data): retval[::2, ::2, ::2] = data return retval + +def explode(data): + """ + Expands a 3D array by adding new voxels between existing ones. + This is used to create the gaps in the 3D plot. + """ + shape = data.shape + new_shape = (2 * shape[0] - 1, 2 * shape[1] - 1, 2 * shape[2] - 1) + new_data = np.zeros(new_shape, dtype=data.dtype) + new_data[::2, ::2, ::2] = data + return new_data + def plot_expression(arousal, dominance, valence): '''_h = cuda tensor (N_PIX, N_PIX, N_PIX)''' @@ -302,7 +270,8 @@ def plot_expression(arousal, dominance, valence): y[:, 1::2, :] += 1 z[:, :, 1::2] += 1 - ax = plt.figure().add_subplot(projection='3d') + fig = plt.figure() + ax = fig.add_subplot(projection='3d') f_2 = np.ones([2 * N_PIX - 1, 2 * N_PIX - 1, @@ -313,7 +282,6 @@ def plot_expression(arousal, dominance, valence): f_2[:, :, :, 3] = f_2[:, :, :, 3].clip(.01, .74) - print(f_2.shape, 'f_2 AAAA') ecolors_2 = f_2 ax.voxels(x, y, z, filled_2, facecolors=f_2, edgecolors=.006 * ecolors_2) @@ -348,10 +316,156 @@ def plot_expression(arousal, dominance, valence): ax.set_xlim(0, N_PIX) ax.set_ylim(0, N_PIX) ax.set_zlim(0, N_PIX) + # plt.show() + +# TTS +VOICES = [f'wav/{vox}' for vox in os.listdir('wav')] +_tts = StyleTTS2().to('cpu') + +def only_greek_or_only_latin(text, lang='grc'): + ''' + str: The converted string in the specified target script. + Characters not found in any mapping are preserved as is. + Latin accented characters in the input (e.g., 'É', 'ü') will + be preserved in their lowercase form (e.g., 'é', 'ü') if + converting to Latin. + ''' + + # --- Mapping Dictionaries --- + # Keys are in lowercase as input text is case-folded. + # If the output needs to maintain original casing, additional logic is required. + + latin_to_greek_map = { + 'a': 'α', 'b': 'β', 'g': 'γ', 'd': 'δ', 'e': 'ε', + 'ch': 'τσο', # Example of a multi-character Latin sequence + 'z': 'ζ', 'h': 'χ', 'i': 'ι', 'k': 'κ', 'l': 'λ', + 'm': 'μ', 'n': 'ν', 'x': 'ξ', 'o': 'ο', 'p': 'π', + 'v': 'β', 'sc': 'σκ', 'r': 'ρ', 's': 'σ', 't': 'τ', + 'u': 'ου', 'f': 'φ', 'c': 'σ', 'w': 'β', 'y': 'γ', + } + + greek_to_latin_map = { + 'ου': 'ou', # Prioritize common diphthongs/digraphs + 'α': 'a', 'β': 'v', 'γ': 'g', 'δ': 'd', 'ε': 'e', + 'ζ': 'z', 'η': 'i', 'θ': 'th', 'ι': 'i', 'κ': 'k', + 'λ': 'l', 'μ': 'm', 'ν': 'n', 'ξ': 'x', 'ο': 'o', + 'π': 'p', 'ρ': 'r', 'σ': 's', 'τ': 't', 'υ': 'y', # 'y' is a common transliteration for upsilon + 'φ': 'f', 'χ': 'ch', 'ψ': 'ps', 'ω': 'o', + 'ς': 's', # Final sigma + } + + cyrillic_to_latin_map = { + 'а': 'a', 'б': 'b', 'в': 'v', 'г': 'g', 'д': 'd', 'е': 'e', 'ё': 'yo', 'ж': 'zh', + 'з': 'z', 'и': 'i', 'й': 'y', 'к': 'k', 'л': 'l', 'м': 'm', 'н': 'n', 'о': 'o', + 'п': 'p', 'р': 'r', 'с': 's', 'т': 't', 'у': 'u', 'ф': 'f', 'х': 'kh', 'ц': 'ts', + 'ч': 'ch', 'ш': 'sh', 'щ': 'shch', 'ъ': '', 'ы': 'y', 'ь': '', 'э': 'e', 'ю': 'yu', + 'я': 'ya', + } + + # Direct Cyrillic to Greek mapping based on phonetic similarity. + # These are approximations and may not be universally accepted transliterations. + cyrillic_to_greek_map = { + 'а': 'α', 'б': 'β', 'в': 'β', 'г': 'γ', 'д': 'δ', 'е': 'ε', 'ё': 'ιο', 'ж': 'ζ', + 'з': 'ζ', 'и': 'ι', 'й': 'ι', 'κ': 'κ', 'λ': 'λ', 'м': 'μ', 'н': 'ν', 'о': 'ο', + 'π': 'π', 'ρ': 'ρ', 'σ': 'σ', 'τ': 'τ', 'у': 'ου', 'ф': 'φ', 'х': 'χ', 'ц': 'τσ', + 'ч': 'τσ', # or τζ depending on desired sound + 'ш': 'σ', 'щ': 'σ', # approximations + 'ъ': '', 'ы': 'ι', 'ь': '', 'э': 'ε', 'ю': 'ιου', + 'я': 'ια', + } + + # Convert the input text to lowercase, preserving accents for Latin characters. + # casefold() is used for more robust caseless matching across Unicode characters. + lowercased_text = text.lower() #casefold() + output_chars = [] + current_index = 0 + + if lang == 'grc': + # Combine all relevant maps for direct lookup to Greek + conversion_map = {**latin_to_greek_map, **cyrillic_to_greek_map} + + # Sort keys by length in reverse order to handle multi-character sequences first + sorted_source_keys = sorted( + list(latin_to_greek_map.keys()) + list(cyrillic_to_greek_map.keys()), + key=len, + reverse=True + ) + + while current_index < len(lowercased_text): + found_conversion = False + for key in sorted_source_keys: + if lowercased_text.startswith(key, current_index): + output_chars.append(conversion_map[key]) + current_index += len(key) + found_conversion = True + break + if not found_conversion: + # If no specific mapping found, append the character as is. + # This handles unmapped characters and already Greek characters. + output_chars.append(lowercased_text[current_index]) + current_index += 1 + return ''.join(output_chars) + + else: # Default to 'lat' conversion + # Combine Greek to Latin and Cyrillic to Latin maps. + # Cyrillic map keys will take precedence in case of overlap if defined after Greek. + combined_to_latin_map = {**greek_to_latin_map, **cyrillic_to_latin_map} + + # Sort all relevant source keys by length in reverse for replacement + sorted_source_keys = sorted( + list(greek_to_latin_map.keys()) + list(cyrillic_to_latin_map.keys()), + key=len, + reverse=True + ) + + while current_index < len(lowercased_text): + found_conversion = False + for key in sorted_source_keys: + if lowercased_text.startswith(key, current_index): + latin_equivalent = combined_to_latin_map[key] - #plt.show() + # Strip accents ONLY if the source character was from the Greek map. + # This preserves accents on original Latin characters (like 'é') + # and allows for intentional accent stripping from Greek transliterations. + if key in greek_to_latin_map: + normalized_latin = unicodedata.normalize('NFD', latin_equivalent) + stripped_latin = ''.join(c for c in normalized_latin if not unicodedata.combining(c)) + output_chars.append(stripped_latin) + else: + output_chars.append(latin_equivalent) -# ------ + current_index += len(key) + found_conversion = True + break + + if not found_conversion: + # If no conversion happened from Greek or Cyrillic, append the character as is. + # This preserves existing Latin characters (including accented ones from input), + # numbers, punctuation, and other symbols. + output_chars.append(lowercased_text[current_index]) + current_index += 1 + + return ''.join(output_chars) + + +def other_tts(text='Hallov worlds Far over the', + ref_s='wav/af_ZA_google-nwu_0184.wav'): + + text = only_greek_or_only_latin(text, lang='eng') + + x = _tts.inference(text, ref_s=ref_s)[0, 0, :].cpu().numpy() + + # x /= np.abs(x).max() + 1e-7 ~ Volume normalisation @api.py:tts_multi_sentence() OR demo.py + + tmp_file = f'_speech.wav' # N x clients (cleanup vs tmp file / client) + + audiofile.write(tmp_file, x, 24000) + + return tmp_file + + +def update_selected_voice(voice_filename): + return 'wav/' + voice_filename + '.wav' description = ( @@ -366,7 +480,52 @@ description = ( ) with gr.Blocks() as demo: - with gr.Tab(label="Speech analysis"): + with gr.Tab(label="other TTS"): + + selected_voice = gr.State(value='wav/en_US_m-ailabs_mary_ann.wav') + + with gr.Row(): + voice_info = gr.Markdown(f'TTS vox : `{selected_voice.value}`') + + # Main input and output components + with gr.Row(): + text_input = gr.Textbox( + label="Enter text for TTS:", + placeholder="Type your message here...", + lines=4, + value="Farover the misty mountains cold too dungeons deep and caverns old.", + ) + generate_button = gr.Button("Generate Audio", variant="primary") + + output_audio = gr.Audio(label="TTS Output") + + with gr.Column(): + voice_buttons = [] + for i in range(0, len(VOICES), 7): + with gr.Row(): + for voice_filename in VOICES[i:i+7]: + voice_filename = voice_filename[4:-4] # drop wav/ for visibility + button = gr.Button(voice_filename) + + button.click( + fn=update_selected_voice, + inputs=[gr.Textbox(value=voice_filename, visible=False)], + outputs=[selected_voice] + ) + button.click( + fn=lambda v=voice_filename: f"TTS Vox = `{v}`", + inputs=None, + outputs=voice_info + ) + voice_buttons.append(button) + + generate_button.click( + fn=other_tts, + inputs=[text_input, selected_voice], + outputs=output_audio + ) + + with gr.Tab(label="Speech Analysis"): with gr.Row(): with gr.Column(): gr.Markdown(description) @@ -378,10 +537,10 @@ with gr.Blocks() as demo: ) gr.Examples( [ - "female-46-neutral.wav", - "female-20-happy.wav", - "male-60-angry.wav", - "male-27-sad.wav", + "wav/female-46-neutral.wav", + "wav/female-20-happy.wav", + "wav/male-60-angry.wav", + "wav/male-27-sad.wav", ], [input], label="Examples from CREMA-D, ODbL v1.0 license", diff --git a/espeak_util.py b/espeak_util.py new file mode 100644 index 0000000000000000000000000000000000000000..2f057b7342556bdbea8844d68375231948d4b2b9 --- /dev/null +++ b/espeak_util.py @@ -0,0 +1,206 @@ +import platform +import subprocess +import shutil +from pathlib import Path +import os +from typing import Optional, Tuple +from phonemizer.backend.espeak.wrapper import EspeakWrapper + + +class EspeakConfig: + """Utility class for configuring espeak-ng library and binary.""" + + @staticmethod + def find_espeak_binary() -> tuple[bool, Optional[str]]: + """ + Find espeak-ng binary using multiple methods. + + Returns: + tuple: (bool indicating if espeak is available, path to espeak binary if found) + """ + # Common binary names + binary_names = ["espeak-ng", "espeak"] + if platform.system() == "Windows": + binary_names = ["espeak-ng.exe", "espeak.exe"] + + # Common installation directories for Linux + linux_paths = [ + "/usr/bin", + "/usr/local/bin", + "/usr/lib/espeak-ng", + "/usr/local/lib/espeak-ng", + "/opt/espeak-ng/bin", + ] + + # First check if it's in PATH + for name in binary_names: + espeak_path = shutil.which(name) + if espeak_path: + return True, espeak_path + + # For Linux, check common installation directories + if platform.system() == "Linux": + for directory in linux_paths: + for name in binary_names: + path = Path(directory) / name + if path.exists(): + return True, str(path) + + # Try running the command directly as a last resort + try: + subprocess.run( + ["espeak-ng", "--version"], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + check=True, + ) + return True, "espeak-ng" + except (subprocess.SubprocessError, FileNotFoundError): + pass + + return False, None + + @staticmethod + def find_library_path() -> Optional[str]: + """ + Find the espeak-ng library using multiple search methods. + + Returns: + Optional[str]: Path to the library if found, None otherwise + """ + system = platform.system() + + if system == "Linux": + lib_names = ["libespeak-ng.so", "libespeak-ng.so.1"] + common_paths = [ + # Debian/Ubuntu paths + "/usr/lib/x86_64-linux-gnu", + "/usr/lib/aarch64-linux-gnu", # For ARM64 + "/usr/lib/arm-linux-gnueabihf", # For ARM32 + "/usr/lib", + "/usr/local/lib", + # Fedora/RHEL paths + "/usr/lib64", + "/usr/lib32", + # Common additional paths + "/usr/lib/espeak-ng", + "/usr/local/lib/espeak-ng", + "/opt/espeak-ng/lib", + ] + + # Check common locations first + for path in common_paths: + for lib_name in lib_names: + lib_path = Path(path) / lib_name + if lib_path.exists(): + return str(lib_path) + + # Search system library paths + try: + # Use ldconfig to find the library + result = subprocess.run( + ["ldconfig", "-p"], capture_output=True, text=True, check=True + ) + for line in result.stdout.splitlines(): + if "libespeak-ng.so" in line: + # Extract path from ldconfig output + return line.split("=>")[-1].strip() + except (subprocess.SubprocessError, FileNotFoundError): + pass + + elif system == "Darwin": # macOS + common_paths = [ + Path("/opt/homebrew/lib/libespeak-ng.dylib"), + Path("/usr/local/lib/libespeak-ng.dylib"), + *list( + Path("/opt/homebrew/Cellar/espeak-ng").glob( + "*/lib/libespeak-ng.dylib" + ) + ), + *list( + Path("/usr/local/Cellar/espeak-ng").glob("*/lib/libespeak-ng.dylib") + ), + ] + + for path in common_paths: + if path.exists(): + return str(path) + + elif system == "Windows": + common_paths = [ + Path(os.environ.get("PROGRAMFILES", "C:\\Program Files")) + / "eSpeak NG" + / "libespeak-ng.dll", + Path(os.environ.get("PROGRAMFILES(X86)", "C:\\Program Files (x86)")) + / "eSpeak NG" + / "libespeak-ng.dll", + *[ + Path(p) / "libespeak-ng.dll" + for p in os.environ.get("PATH", "").split(os.pathsep) + ], + ] + + for path in common_paths: + if path.exists(): + return str(path) + + return None + + @classmethod + def configure_espeak(cls) -> Tuple[bool, str]: + """ + Configure espeak-ng for use with the phonemizer. + + Returns: + Tuple[bool, str]: (Success status, Status message) + """ + # First check if espeak binary is available + espeak_available, espeak_path = cls.find_espeak_binary() + if not espeak_available: + raise FileNotFoundError( + "Could not find espeak-ng binary. Please install espeak-ng:\n" + "Ubuntu/Debian: sudo apt-get install espeak-ng espeak-ng-data\n" + "Fedora: sudo dnf install espeak-ng\n" + "Arch: sudo pacman -S espeak-ng\n" + "MacOS: brew install espeak-ng\n" + "Windows: Download from https://github.com/espeak-ng/espeak-ng/releases" + ) + + # Find the library + library_path = cls.find_library_path() + if not library_path: + # On Linux, we might not need to explicitly set the library path + if platform.system() == "Linux": + return True, f"Using system espeak-ng installation at: {espeak_path}" + else: + raise FileNotFoundError( + "Could not find espeak-ng library. Please ensure espeak-ng is properly installed." + ) + + # Try to set the library path + try: + EspeakWrapper.set_library(library_path) + return True, f"Successfully configured espeak-ng library at: {library_path}" + except Exception as e: + if platform.system() == "Linux": + # On Linux, try to continue without explicit library path + return True, f"Using system espeak-ng installation at: {espeak_path}" + else: + raise RuntimeError(f"Failed to configure espeak-ng library: {str(e)}") + + +def setup_espeak(): + """ + Set up espeak-ng for use with the phonemizer. + Raises appropriate exceptions if setup fails. + """ + try: + success, message = EspeakConfig.configure_espeak() + print(message) + except Exception as e: + print(f"Error configuring espeak-ng: {str(e)}") + raise + + +# Replace the original set_espeak_library function with this +set_espeak_library = setup_espeak diff --git a/requirements.txt b/requirements.txt index 4ec1303a18cef2c493c152f0af698afb3b322e93..fa09ff07c757975dacacbf41a0fca7e590d128eb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,13 @@ -audiofile -audresample -matplotlib torch +nltk +pydantic==2.10.6 +librosa transformers +phonemizer +audiofile +matplotlib +numpy<2.0.0 +gradio==5.27.0 +audresample + + diff --git a/wav/af_ZA_google-nwu_0184.wav b/wav/af_ZA_google-nwu_0184.wav new file mode 100644 index 0000000000000000000000000000000000000000..1d0155d0aa1d4d5f7d151355e14ffbb46250016e Binary files /dev/null and b/wav/af_ZA_google-nwu_0184.wav differ diff --git a/wav/af_ZA_google-nwu_1919.wav b/wav/af_ZA_google-nwu_1919.wav new file mode 100644 index 0000000000000000000000000000000000000000..379d9afd8c056cc521a9fad0e737adc97979aacd Binary files /dev/null and b/wav/af_ZA_google-nwu_1919.wav differ diff --git a/wav/af_ZA_google-nwu_2418.wav b/wav/af_ZA_google-nwu_2418.wav new file mode 100644 index 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