Update app.py

app.py

@@ -1,23 +1,72 @@
 import gradio as gr
 from transformers import pipeline
-from TTS.api import TTS
+# from TTS.api import TTS
+
+import librosa
+import numpy as np
+import torch
+
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+
+
+checkpoint = "microsoft/speecht5_tts"
+processor = SpeechT5Processor.from_pretrained(checkpoint)
+model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+def tts(text):
+    if len(text.strip()) == 0:
+        return (16000, np.zeros(0).astype(np.int16))
+
+    inputs = processor(text=text, return_tensors="pt")
+
+    # limit input length
+    input_ids = inputs["input_ids"]
+    input_ids = input_ids[..., :model.config.max_text_positions]
+
+    # if speaker == "Surprise Me!":
+    #     # load one of the provided speaker embeddings at random
+    #     idx = np.random.randint(len(speaker_embeddings))
+    #     key = list(speaker_embeddings.keys())[idx]
+    #     speaker_embedding = np.load(speaker_embeddings[key])
+
+    #     # randomly shuffle the elements
+    #     np.random.shuffle(speaker_embedding)
+
+    #     # randomly flip half the values
+    #     x = (np.random.rand(512) >= 0.5) * 1.0
+    #     x[x == 0] = -1.0
+    #     speaker_embedding *= x
+
+        #speaker_embedding = np.random.rand(512).astype(np.float32) * 0.3 - 0.15
+    # else:
+    speaker_embedding = np.load("cmu_us_bdl_arctic-wav-arctic_a0009.npy")
+
+    speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)
+
+    speech = model.generate_speech(input_ids, speaker_embedding, vocoder=vocoder)
+
+    speech = (speech.numpy() * 32767).astype(np.int16)
+    return (16000, speech)
+
 
 captioner = pipeline(model="microsoft/git-base")
-tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=False)
+# tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=False)
 
 
 def predict(image):
     text = captioner(image)[0]["generated_text"]
 
-    audio_output = "output.wav"
-    tts.tts_to_file(text, speaker=tts.speakers[0], language="en", file_path=audio_output)
+    # audio_output = "output.wav"
+    # tts.tts_to_file(text, speaker=tts.speakers[0], language="en", file_path=audio_output)
+    audio = tts(text)
     
-    return text, audio_output
+    return text, audio
 
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="pil"),
-    outputs=['text', gr.Audio()]
+    outputs=['text', gr.Audio(type="numpy")]
 )
 
 demo.launch()