Update mai.py

mai.py

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+from transformers import pipeline
+import torch
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset
+from IPython.display import Audio
+device = "cuda" if torch.cuda.is_available() else "cpu"
+from transformers import pipeline
+
+pipe = pipeline(
+    "automatic-speech-recognition", model="openai/whisper-base", device=device
+)
+
+dataset = load_dataset("facebook/voxpopuli", "es", split="validation", streaming=True)
+sample = next(iter(dataset))
+def translate(audio):
+    outputs = pipe(audio, generate_kwargs={"task": "translate",'max_new_tokens':255})
+    return outputs["text"]
+# Load SpeechT5 Processor and Model
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
+
+# Load Speaker Embeddings
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[0]["xvector"]).unsqueeze(0).to(device)
+# Define Speech Synthesis Function
+def synthesise(text):
+    inputs = processor(text=text, return_tensors="pt")
+    speech = model.generate_speech(
+        inputs["input_ids"].to(device),
+        speaker_embeddings,
+        vocoder=vocoder
+    )
+    return speech.cpu()
+import numpy as np
+
+target_dtype = np.int16
+max_range = np.iinfo(target_dtype).max
+def speech_to_speech_translation(audio):
+    translated_text = translate(audio)
+    synthesised_speech = synthesise(translated_text)
+    synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
+    return 16000, synthesised_speech
+sampling_rate, synthesised_speech = speech_to_speech_translation(sample["audio"])