Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from transformers import pipeline, WhisperForConditionalGeneration, WhisperProcessor
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset
+
+# Check if a GPU is available and set the device
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+# Load the Whisper ASR model
+whisper_model_id = "riteshkr/quantized-whisper-large-v3"
+whisper_model = WhisperForConditionalGeneration.from_pretrained(whisper_model_id)
+whisper_processor = WhisperProcessor.from_pretrained(whisper_model_id)
+
+# Set the language to English using forced_decoder_ids
+forced_decoder_ids = whisper_processor.get_decoder_prompt_ids(language="english", task="transcribe")
+
+whisper_pipe = pipeline(
+    "automatic-speech-recognition",
+    model=whisper_model,
+    tokenizer=whisper_processor.tokenizer,
+    feature_extractor=whisper_processor.feature_extractor,
+    device=0 if torch.cuda.is_available() else -1
+)
+
+# Load the SpeechT5 TTS model
+tts_processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+tts_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+tts_model.to(device)
+vocoder.to(device)
+
+# Load speaker embeddings for TTS
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0).to(device)
+
+# Set target data type and max range for speech
+target_dtype = np.int16
+max_range = np.iinfo(target_dtype).max
+
+# Define the transcription function (Whisper ASR)
+def transcribe_speech(filepath):
+    batch_size = 16 if torch.cuda.is_available() else 4
+    output = whisper_pipe(
+        filepath,
+        max_new_tokens=256,
+        generate_kwargs={"forced_decoder_ids": forced_decoder_ids},
+        chunk_length_s=30,
+        batch_size=batch_size,
+    )
+    return output["text"]
+
+# Define the synthesis function (SpeechT5 TTS)
+def synthesise(text):
+    inputs = tts_processor(text=text, return_tensors="pt")
+    speech = tts_model.generate_speech(
+        inputs["input_ids"].to(device), speaker_embeddings, vocoder=vocoder
+    )
+    return speech.cpu()
+
+# Define the speech-to-speech translation function
+def speech_to_speech_translation(audio):
+    # Transcribe speech
+    translated_text = transcribe_speech(audio)
+    # Synthesize speech
+    synthesised_speech = synthesise(translated_text)
+    # Convert speech to desired format
+    synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
+    return 16000, synthesised_speech
+
+# Define the Gradio interfaces for microphone input and file upload
+mic_translate = gr.Interface(
+    fn=speech_to_speech_translation,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs=gr.Audio(label="Generated Speech", type="numpy"),
+)
+
+file_translate = gr.Interface(
+    fn=speech_to_speech_translation,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs=gr.Audio(label="Generated Speech", type="numpy"),
+)
+
+# Define the Gradio interfaces for transcription
+mic_transcribe = gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs=gr.Textbox(),
+)
+
+file_transcribe = gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs=gr.Textbox(),
+)
+
+# Create the app using Gradio Blocks with tabbed interfaces
+demo = gr.Blocks()
+
+with demo:
+    gr.TabbedInterface(
+        [
+            mic_transcribe, file_transcribe,  # For transcription
+            mic_translate, file_translate  # For speech-to-speech translation
+        ],
+        [
+            "Transcribe Microphone", "Transcribe Audio File",
+            "Translate Microphone", "Translate Audio File"
+        ]
+    )
+
+# Launch the app with debugging enabled
+if __name__ == "__main__":
+    demo.launch(debug=True, share=True)