Added Whisper TTS

src/text2speech/Whisper_TTS/README.md

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+Converting text input to speech given the input language 
+# Whisper TTS model
+
+This project uses WhisperSpeech to convert text to speech, with an option for voice cloning.
+reference - https://github.com/collabora/WhisperSpeech
+
+## Setup and Installation
+
+1. Create and activate a virtual environment:
+    - Linux
+        - python -m venv venv
+        - source venv/bin/activate
+    - Windows
+        - python -m venv venv
+        - venv\Scripts\activate
+
+2. Install the required packages:
+    - ```bash
+        pip install -r requirements.txt
+        ```
+3.  Usage
+
+    You can add the reference audio from a drive download link to create a more personalized transcriptions or a  pass a direct audio file path.
+    - voice with cloning
+        https://github.com/user-attachments/assets/fa4bc138-e17a-4e1c-a88a-8dd5f37e0db4
+
+
+    - output without cloning
+        https://github.com/user-attachments/assets/1bb4ee27-3730-4cca-85f0-f72629d7c640
+

src/text2speech/Whisper_TTS/requirements.txt

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+torch
+torchaudio
+whisperspeech
+requests

src/text2speech/Whisper_TTS/whisper_tts.py

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+import torch
+from whisperspeech.pipeline import Pipeline
+import torchaudio
+import os
+import requests
+
+def download_reference_audio(url, output_path):
+    response = requests.get(url)
+    if response.status_code == 200:
+        with open(output_path, "wb") as file:
+            file.write(response.content)
+        print(f"File downloaded and saved to {output_path}")
+        return True
+    else:
+        print(f"Failed to download the file. Status code: {response.status_code}")
+        return False
+
+def initialize_pipeline():
+    return Pipeline(s2a_ref='collabora/whisperspeech:s2a-q4-tiny-en+pl.model')
+
+def text_to_speech(pipe, text, reference_audio_path=None, cps=10.5):
+    if reference_audio_path:
+        audio = pipe.generate(text, lang='en', cps=cps, speaker=reference_audio_path)
+    else:
+        audio = pipe.generate(text, lang='en', cps=cps)
+
+    audio_cpu = audio.cpu().squeeze()
+    if audio_cpu.dim() == 1:
+        audio_cpu = audio_cpu.unsqueeze(0)
+
+    return audio_cpu
+
+def save_audio(audio, output_file, sample_rate=24000):
+    torchaudio.save(output_file, audio, sample_rate=sample_rate, encoding="PCM_F")
+    print(f"Generated audio file: {output_file}")
+
+def transcribe(text, output_file, use_voice_cloning=False, reference_audio_path=None):
+    if use_voice_cloning and not reference_audio_path:
+        print("Error: Voice cloning requested but no reference audio path provided.")
+        return False
+
+    if use_voice_cloning and not os.path.exists(reference_audio_path):
+        print(f"Error: Reference audio file not found at {reference_audio_path}")
+        return False
+
+    pipe = initialize_pipeline()
+
+    audio = text_to_speech(pipe, text, reference_audio_path if use_voice_cloning else None)
+
+    save_audio(audio, output_file)
+
+    return True
+
+if __name__ == "__main__":
+    text_to_transcribe = "This is a test of the WhisperSpeech text-to-speech system with optional voice cloning."
+    output_file = "output.wav"
+    # you can add a add your audio download link from drive for reference audio
+    reference_audio_url = "https://drive.google.com/uc?export=download&id=1P5kM5-U9tk3bdw309ybIbkQoxWw04YpM"
+    reference_audio_path = "reference_audio.wav"
+
+    # Download reference audio if it doesn't exist
+    if not os.path.exists(reference_audio_path):
+        download_reference_audio(reference_audio_url, reference_audio_path)
+
+    # Without voice cloning
+    transcribe(text_to_transcribe, "output_without_cloning.wav")
+
+    # you can add your audio file path as reference_audio_path as wav file
+    transcribe(text_to_transcribe, "output_with_cloning.wav", use_voice_cloning=True, reference_audio_path=reference_audio_path)