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| import os | |
| import azure.cognitiveservices.speech as speechsdk | |
| from time import sleep | |
| from datetime import datetime | |
| import gradio as gr | |
| # Get environment settings | |
| speech_key, service_region = os.environ['SPEECH__SERVICE__KEY'], os.environ['SPEECH__SERVICE__REGION'] | |
| # Define languages – source is Chinese, target is US English | |
| from_language, to_languages = 'zh-CN', ['fi-FI'] | |
| def synthesize_audio(text, voice="fi-FI-NooraNeural", output_audio="translation_audio.wav"): | |
| speech_config = speechsdk.SpeechConfig(subscription=speech_key, region=service_region) | |
| speech_config.speech_synthesis_voice_name = voice | |
| audio_config = speechsdk.audio.AudioOutputConfig(filename=output_audio) | |
| synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config) | |
| result = synthesizer.speak_text_async(text).get() | |
| if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted: | |
| print(f"Audio synthesized and saved to: {output_audio}") | |
| else: | |
| print("Synthesis failed: ", result.error_details) | |
| def get_result_text(reason, translation_recognition_result, to_language): | |
| reason_format = { | |
| speechsdk.ResultReason.TranslatedSpeech: | |
| f'RECOGNIZED "{from_language}": {translation_recognition_result.text}\n' + | |
| f'TRANSLATED into "{to_language}": {translation_recognition_result.translations[to_language]}', | |
| speechsdk.ResultReason.RecognizedSpeech: f'Recognized: "{translation_recognition_result.text}"', | |
| speechsdk.ResultReason.NoMatch: f'No speech could be recognized: {translation_recognition_result.no_match_details}', | |
| speechsdk.ResultReason.Canceled: f'Speech Recognition canceled: {translation_recognition_result.cancellation_details}' | |
| } | |
| return reason_format.get(reason, 'Unable to recognize speech') | |
| def process_audio(input_audio_path): | |
| """ | |
| Process the uploaded audio file: | |
| 1. Translate the audio using continuous recognition. | |
| 2. Synthesize the translated text into audio. | |
| 3. Return the translated text (for preview) and synthesized audio file. | |
| """ | |
| # Set up translation configuration | |
| translation_config = speechsdk.translation.SpeechTranslationConfig( | |
| subscription=speech_key, region=service_region) | |
| translation_config.speech_recognition_language = from_language | |
| for lang in to_languages: | |
| translation_config.add_target_language(lang) | |
| # Use the uploaded file's path for translation | |
| audio_config = speechsdk.audio.AudioConfig(filename=input_audio_path) | |
| translation_recognizer = speechsdk.translation.TranslationRecognizer( | |
| translation_config=translation_config, audio_config=audio_config) | |
| done = False | |
| translations = [] | |
| stop_called = False # flag to avoid duplicate stop processing | |
| def handle_result(evt): | |
| # Print and store translated segments | |
| if evt.result.reason == speechsdk.ResultReason.TranslatedSpeech: | |
| text_seg = evt.result.translations[to_languages[0]] | |
| print("Segment: ", text_seg) | |
| translations.append(text_seg) | |
| def stop_cb(evt): | |
| nonlocal done, stop_called | |
| if stop_called: | |
| return | |
| stop_called = True | |
| done = True | |
| # Connect events | |
| translation_recognizer.recognized.connect(handle_result) | |
| translation_recognizer.session_stopped.connect(stop_cb) | |
| translation_recognizer.canceled.connect(stop_cb) | |
| # Start recognition | |
| translation_recognizer.start_continuous_recognition() | |
| while not done: | |
| sleep(0.5) | |
| translation_recognizer.stop_continuous_recognition() | |
| # Join all translated segments into one final text | |
| final_text = '\n'.join(translations) | |
| print("Final Translated Text:\n", final_text) | |
| # Synthesize the translated text into audio | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| synthesized_audio_file = f"translation_audio_{from_language}_to_{to_languages[0]}_{timestamp}.wav" | |
| synthesize_audio(final_text, voice="fi-FI-NooraNeural", output_audio=synthesized_audio_file) | |
| # Return both the translated text and the path to the synthesized audio file. | |
| return final_text, synthesized_audio_file | |
| # Create a Gradio Interface | |
| iface = gr.Interface( | |
| fn=process_audio, | |
| inputs=gr.Audio(sources=["upload"], type="filepath", label="Upload Audio"), | |
| outputs=[gr.Textbox(label="Translated Text Preview"), gr.Audio(type="filepath", label="Translated Audio")], | |
| title="Speech Translation and Synthesis", | |
| description="Upload an audio file containing Chinese speech. The app translates the speech to US English and synthesizes the translated text into audio for playback and download." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |