Create app.py

app.py

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+import gradio as gr
+import torch
+import torchaudio
+
+from transformers import AutoProcessor, SeamlessM4TModel
+processor = AutoProcessor.from_pretrained("facebook/hf-seamless-m4t-medium")
+model = SeamlessM4TModel.from_pretrained("facebook/hf-seamless-m4t-medium")
+model.to('cuda')
+
+language_dict  = {
+"Modern Standard Arabic" : "arb",
+"Bengali" : "ben",
+"Catalan" : "cat",
+"Czech" : "ces",
+"Mandarin Chinese" : "cmn",
+"Welsh" : "cym",
+"Danish" : "dan",
+"German" : "deu",
+"English" : "eng",
+"Estonian" : "est",
+"Finnish" : "fin",
+"French" : "fra",
+"Hindi" : "hin",
+"Indonesian" : "ind",
+"Italian" : "ita",
+"Japanese" : "jpn",
+"Korean" : "kor",
+"Maltese" : "mlt",
+"Dutch" : "nld",
+"Western Persian" : "pes",
+"Polish" : "pol",
+"Portuguese" : "por",
+"Romanian" : "ron",
+"Russian" : "rus",
+"Slovak" : "slk",
+"Spanish" : "spa",
+"Swedish" : "swe",
+"Swahili" : "swh",
+"Telugu" : "tel",
+"Tagalog" : "tgl",
+"Thai" : "tha",
+"Turkish" : "tur",
+"Ukrainian" : "ukr",
+"Urdu" : "urd",
+"Northern Uzbek" : "uzn",
+"Vietnamese" : "vie"
+}
+languages = list(language_dict.keys())
+
+
+def png(source_lang,target_lang,audio,text):
+
+  source_lang_code = language_dict[source_lang]
+  target_lang_code = language_dict[target_lang]
+
+  if audio == None:
+    processed_inputs = processor(text, src_lang=source_lang_code, return_tensors="pt")
+  else:
+    sample_rate, audio_data = audio
+    audio_tokens = torch.from_numpy(audio_data).to(torch.device("cuda"))
+    audio_tokens = audio_tokens.to(torch.float32)
+    audio_tokens = torchaudio.functional.resample(audio_tokens, orig_freq=sample_rate, new_freq=16_000)
+    audio_tokens = audio_tokens.cpu()
+    processed_inputs = processor(audios=audio_tokens, sampling_rate=16000, return_tensors="pt")
+
+
+  processed_inputs = processed_inputs.to("cuda")
+  generated_audio = model.generate(**processed_inputs, tgt_lang=target_lang_code)[0].cpu().numpy().squeeze()
+  output_tokens = model.generate(**processed_inputs, tgt_lang=target_lang_code, generate_speech=False)
+  generated_text = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
+
+  return (16000,generated_audio),generated_text
+
+iface = gr.Interface(
+    png,
+    inputs=[
+        gr.Dropdown(languages, label="Source Language"),
+        gr.Dropdown(languages, label="Target Language"),
+        gr.Audio(),
+        gr.Textbox(label="Enter Text in Source Language")
+    ],
+    outputs=[
+        gr.Audio(label = "Translated Audio"),
+        gr.Textbox(label="Translated Text")
+        ],
+    title="Language Translation App",
+    description="Select source and target languages for translation.",
+)
+
+iface.launch(debug=True)
+
+