Update app.py

app.py

@@ -2,194 +2,58 @@ import gradio as gr
 import numpy as np
 import torch
 from datasets import load_dataset
-from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
 
+from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
+#from transformers import VitsModel, VitsTokenizer
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
 # load speech translation checkpoint
-asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2", device=device)
-greek_translation_pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-el")
+asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
 
 # load text-to-speech checkpoint and speaker embeddings
-model_id = "microsoft/speecht5_tts"  # update with your model id
-# pipe = pipeline("automatic-speech-recognition", model=model_id)
-model = SpeechT5ForTextToSpeech.from_pretrained(model_id)
-vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
-speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0)
-
-processor = SpeechT5Processor.from_pretrained(model_id)
-
-model_id_greek = "Sandiago21/speecht5_finetuned_google_fleurs_greek"
-model_greek = SpeechT5ForTextToSpeech.from_pretrained(model_id_greek)
-processor_greek = SpeechT5Processor.from_pretrained(model_id_greek)
-
-replacements = [
-    ("á", "a"),
-    ("â", "a"),
-    ("ã", "a"),
-    ("í", "i"),
-    ("á", "a"),
-    ("í", "i"),
-    ("ñ", "n"),
-    ("ó", "o"),
-    ("ú", "u"),
-    ("ü", "u"),
-    ("á", "a"),
-    ("ç", "c"),
-    ("è", "e"),
-    ("ì", "i"),
-    ("í", "i"),
-    ("ò", "o"),
-    ("ó", "o"),
-    ("ù", "u"),
-    ("ú", "u"),
-    ("š", "s"),
-    ("ï", "i"),
-    ("à", "a"),
-    ("â", "a"),
-    ("ç", "c"),
-    ("è", "e"),
-    ("ë", "e"),
-    ("î", "i"),
-    ("ï", "i"),
-    ("ô", "o"),
-    ("ù", "u"),
-    ("û", "u"),
-    ("ü", "u"),
-    ("ου", "u"),
-    ("αυ", "af"),
-    ("ευ", "ef"),
-    ("ει", "i"),
-    ("οι", "i"),
-    ("αι", "e"),
-    ("ού", "u"),
-    ("εί", "i"),
-    ("οί", "i"),
-    ("αί", "e"),
-    ("Ά", "A"),
-    ("Έ", "E"),
-    ("Ή", "H"),
-    ("Ί", "I"),
-    ("Ό", "O"),
-    ("Ύ", "Y"),
-    ("Ώ", "O"),
-    ("ΐ", "i"),
-    ("Α", "A"),
-    ("Β", "B"),
-    ("Γ", "G"),
-    ("Δ", "L"),
-    ("Ε", "Ε"),
-    ("Ζ", "Z"),
-    ("Η", "I"),
-    ("Θ", "Th"),
-    ("Ι", "I"),
-    ("Κ", "K"),
-    ("Λ", "L"),
-    ("Μ", "M"),
-    ("Ν", "N"),
-    ("Ξ", "Ks"),
-    ("Ο", "O"),
-    ("Π", "P"),
-    ("Ρ", "R"),
-    ("Σ", "S"),
-    ("Τ", "T"),
-    ("Υ", "Y"),
-    ("Φ", "F"),
-    ("Χ", "X"),
-    ("Ω", "O"),
-    ("ά", "a"),
-    ("έ", "e"),
-    ("ή", "i"),
-    ("ί", "i"),
-    ("α", "a"),
-    ("β", "v"),
-    ("γ", "g"),
-    ("δ", "d"),
-    ("ε", "e"),
-    ("ζ", "z"),
-    ("η", "i"),
-    ("θ", "th"),
-    ("ι", "i"),
-    ("κ", "k"),
-    ("λ", "l"),
-    ("μ", "m"),
-    ("ν", "n"),
-    ("ξ", "ks"),
-    ("ο", "o"),
-    ("π", "p"),
-    ("ρ", "r"),
-    ("ς", "s"),
-    ("σ", "s"),
-    ("τ", "t"),
-    ("υ", "i"),
-    ("φ", "f"),
-    ("χ", "h"),
-    ("ψ", "ps"),
-    ("ω", "o"),
-    ("ϊ", "i"),
-    ("ϋ", "i"),
-    ("ό", "o"),
-    ("ύ", "i"),
-    ("ώ", "o"),
-    ("í", "i"),
-    ("õ", "o"),
-    ("Ε", "E"),
-    ("Ψ", "Ps"),
-]
-
-def cleanup_text(text):
-    for src, dst in replacements:
-        text = text.replace(src, dst)
-    return text
-
-
-def synthesize_speech(text):
-    text = cleanup_text(text)
-    inputs = processor(text=text, return_tensors="pt")
-    speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
 
-    return gr.Audio.update(value=(16000, speech.cpu().numpy()))
 
 
-def translate_to_english(audio):
-    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate", "language": "english"})
-    return outputs["text"]
+#model = VitsModel.from_pretrained("Matthijs/mms-tts-deu")
+#tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-deu")
 
 
-def synthesise_from_english(text):
-    text = cleanup_text(text)
-    inputs = processor(text=text, return_tensors="pt")
-    speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
-    return speech.cpu().numpy()
+#processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+processor = SpeechT5Processor.from_pretrained("kfahn/speecht5_finetuned_voxpopuli_es")
+#processor = SpeechT5Processor.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl")
+
+#model = SpeechT5ForTextToSpeech.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl").to(device)
+model = SpeechT5ForTextToSpeech.from_pretrained("kfahn/speecht5_finetuned_voxpopuli_es").to(device)
+#model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
 
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
 
-def translate_from_english_to_greek(text):
-    return greek_translation_pipe(text)[0]["translation_text"]
 
+def translate(audio):
+    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "es"})
+    return outputs["text"]
 
-def synthesise_from_greek(text):
-    text = cleanup_text(text)
-    inputs = processor_greek(text=text, return_tensors="pt")
-    speech = model_greek.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
+
+def synthesise(text):
+    inputs = processor(text=text, return_tensors="pt")
+    speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
     return speech.cpu()
 
 
 def speech_to_speech_translation(audio):
-    translated_text = translate_to_english(audio)
-    translated_text = translate_from_english_to_greek(translated_text)
-#     synthesised_speech = synthesise_from_english(translated_text)
-#     translated_text = translate_from_english_to_greek(synthesised_speech)
-    synthesised_speech = synthesise_from_greek(translated_text)
+    translated_text = translate(audio)
+    synthesised_speech = synthesise(translated_text)
     synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
-    return ((16000, synthesised_speech), translated_text)
+    return 16000, synthesised_speech
 
 
 title = "Cascaded STST"
 description = """
-Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Greek. Demo uses OpenAI's [Whisper Large v2](https://huggingface.co/openai/whisper-large-v2) model for speech translation, and [Sandiago21/speecht5_finetuned_google_fleurs_greek](https://huggingface.co/Sandiago21/speecht5_finetuned_google_fleurs_greek) checkpoint for text-to-speech, which is based on Microsoft's
-[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech, fine-tuned in Greek Audio dataset:
+Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in English. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Microsoft's
+[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech:
 ![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation")
 """
 
@@ -198,7 +62,7 @@ demo = gr.Blocks()
 mic_translate = gr.Interface(
     fn=speech_to_speech_translation,
     inputs=gr.Audio(source="microphone", type="filepath"),
-    outputs=[gr.Audio(label="Generated Speech", type="numpy"), gr.outputs.Textbox()],
+    outputs=gr.Audio(label="Generated Speech", type="numpy"),
     title=title,
     description=description,
 )
@@ -206,7 +70,7 @@ mic_translate = gr.Interface(
 file_translate = gr.Interface(
     fn=speech_to_speech_translation,
     inputs=gr.Audio(source="upload", type="filepath"),
-    outputs=[gr.Audio(label="Generated Speech", type="numpy"), gr.outputs.Textbox()],
+    outputs=gr.Audio(label="Generated Speech", type="numpy"),
     examples=[["./example.wav"]],
     title=title,
     description=description,