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| import gradio as gr | |
| import wave | |
| import numpy as np | |
| from io import BytesIO | |
| from huggingface_hub import hf_hub_download | |
| from piper import PiperVoice | |
| from transformers import pipeline | |
| import hazm | |
| import typing | |
| normalizer = hazm.Normalizer() | |
| sent_tokenizer = hazm.SentenceTokenizer() | |
| word_tokenizer = hazm.WordTokenizer() | |
| tagger_path = hf_hub_download(repo_id="gyroing/HAZM_POS_TAGGER", filename="pos_tagger.model") | |
| tagger = hazm.POSTagger(model=tagger_path) | |
| model_path = hf_hub_download(repo_id="gyroing/Persian-Piper-Model-gyro", filename="fa_IR-gyro-medium.onnx") | |
| config_path = hf_hub_download(repo_id="gyroing/Persian-Piper-Model-gyro", filename="fa_IR-gyro-medium.onnx.json") | |
| voice = PiperVoice.load(model_path, config_path) | |
| def preprocess_text(text: str) -> typing.List[typing.List[str]]: | |
| """Split/normalize text into sentences/words with hazm""" | |
| text = normalizer.normalize(text) | |
| processed_sentences = [] | |
| for sentence in sent_tokenizer.tokenize(text): | |
| words = word_tokenizer.tokenize(sentence) | |
| processed_words = fix_words(words) | |
| processed_sentences.append(" ".join(processed_words)) | |
| return " ".join(processed_sentences) | |
| def fix_words(words: typing.List[str]) -> typing.List[str]: | |
| fixed_words = [] | |
| for word, pos in tagger.tag(words): | |
| if pos[-1] == "Z": | |
| if word[-1] != "ِ": | |
| if (word[-1] == "ه") and (word[-2] != "ا"): | |
| word += "ی" | |
| word += "ِ" | |
| fixed_words.append(word) | |
| return fixed_words | |
| def synthesize_speech(text): | |
| # Create an in-memory buffer for the WAV file | |
| buffer = BytesIO() | |
| with wave.open(buffer, 'wb') as wav_file: | |
| wav_file.setframerate(voice.config.sample_rate) | |
| wav_file.setsampwidth(2) # 16-bit | |
| wav_file.setnchannels(1) # mono | |
| # Synthesize speech | |
| eztext = preprocess_text(text) | |
| voice.synthesize(eztext, wav_file) | |
| # Convert buffer to NumPy array for Gradio output | |
| buffer.seek(0) | |
| audio_data = np.frombuffer(buffer.read(), dtype=np.int16) | |
| return audio_data.tobytes(), None | |
| # Using Gradio Blocks | |
| with gr.Blocks(theme=gr.themes.Base()) as blocks: | |
| gr.Markdown("# Persian Text to Speech Synthesizer") | |
| gr.Markdown("Enter text to synthesize it into speech using Piper+Hazm") | |
| input_text = gr.Textbox(label="ورود متن") | |
| output_audio = gr.Audio(label="Synthesized Speech", type="numpy") | |
| output_text = gr.Textbox(label="Output Text", visible=False) # This is the new text output component | |
| submit_button = gr.Button("Synthesize") | |
| submit_button.click(synthesize_speech, inputs=input_text, outputs=[output_audio, output_text]) | |
| # Run the app | |
| blocks.launch() |