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README.md

@@ -1,12 +1,6 @@
 ---
-title: Speech To Speech Translation Greek With Transcription
-emoji: 📉
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.37.0
+title: speech-to-speech-translation-greek-with-transcription
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.36.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import numpy as np
+import torch
+from datasets import load_dataset
+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="openai/whisper-large-v2", device=device)
+
+# load text-to-speech checkpoint and speaker embeddings
+model_id = "Sandiago21/speecht5_finetuned_google_fleurs_greek"  # 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)
+
+replacements = [
+    ("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(audio):
+    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "greek"})
+    return outputs["text"]
+
+
+def synthesise(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()
+
+
+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), translated_text)
+
+
+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:
+![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()
+
+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()],
+    title=title,
+    description=description,
+)
+
+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()],
+    examples=[["./example.wav"]],
+    title=title,
+    description=description,
+)
+
+with demo:
+    gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
+
+demo.launch()

packages.txt

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+ffmpeg
+

requirements.txt

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+torch
+git+https://github.com/huggingface/transformers
+datasets
+torchaudio
+sentencepiece
+