Update app.py

app.py

@@ -3,48 +3,50 @@ import numpy as np
 import torch
 from datasets import load_dataset
 
-from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, pipeline
-from transformers import SpeechT5Processor
-
-token = "<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>"
-model_identifier = "tugstugi/mongolian-tts-ljspeech"
-
-processor = SpeechT5Processor.from_pretrained(model_identifier, revision="main", token=token)
+from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
 
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
 # load speech translation checkpoint
-asr_pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=device)
+asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
 
 # load text-to-speech checkpoint and speaker embeddings
-processor = SpeechT5Processor.from_pretrained("tugstugi/mongolian-tts-ljspeech")
-
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
 
+# Using a more lightweight text-to-speech model
 model = SpeechT5ForTextToSpeech.from_pretrained("ttskit/ttskit-tts-ljspeech").to(device)
+
+# Using a more lightweight vocoder
 vocoder = SpeechT5HifiGan.from_pretrained("ljspeech/vocoder-cryptron").to(device)
 
 embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
 speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
 
+
 def translate(audio):
     outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
     return outputs["text"]
 
+
 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(audio)
     synthesised_speech = synthesise(translated_text)
     synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
     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 English. Demo uses Facebook's [Wav2Vec 2.0](https://huggingface.co/facebook/wav2vec2-base-960h) model for speech recognition, and a lightweight text-to-speech model ([ttskit/ttskit-tts-ljspeech](https://huggingface.co/ttskit/ttskit-tts-ljspeech)) along with a lightweight vocoder ([ljspeech/vocoder-cryptron](https://huggingface.co/ljspeech/vocoder-cryptron)).
+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")
 """
 
 demo = gr.Blocks()
@@ -69,4 +71,4 @@ file_translate = gr.Interface(
 with demo:
     gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
 
-demo.launch()
+demo.launch()