Update app.py

app.py

@@ -1,3 +1,9 @@
+!apt-get install -y perl
+!wget https://www.isi.edu/~ulf/uroman/downloads/uroman-v1.2.7.tar.gz
+!mkdir uroman
+!tar -zxvf ./uroman-v1.2.7.tar.gz -C ./uroman
+!chmod +x ./uroman/bin/uroman.pl
+
 import gradio as gr
 import numpy as np
 import torch
@@ -9,22 +15,43 @@ from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Proce
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
 # load speech translation checkpoint
-asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
+asr_pipe = pipeline("automatic-speech-recognition", model="KoRiF/whisper-small-be", device=device)
 
 # load text-to-speech checkpoint and speaker embeddings
 processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
 
-model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
+model = SpeechT5ForTextToSpeech.from_pretrained("KoRiF/speecht5_finetuned_common_voice_be").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(audio):
-    outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
-    return outputs["text"]
-
+def translate(audio, transliteration = lambda txt: txt):
+    outputs = pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "be"})#larusian
+    return transliteration(outputs["text"])
+
+import subprocess
+
+def transliterate_text(text, lang_code=None, use_chart=False, use_cache=True):
+    command = ['perl', './uroman/bin/uroman.pl']
+    if lang_code:
+        command.extend(['-l', lang_code])
+    if use_chart:
+        command.append('--chart')
+    if not use_cache:
+        command.append('--no-cache')
+    
+    process = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+                               universal_newlines=True)
+    output, error = process.communicate(input=text)
+    if (error):
+        print(f"Error: >>> {error}")
+    return output.strip()
+
+language = 'bel'
+def transliterate(text):
+    return transliterate_text(text, language)
 
 def synthesise(text):
     inputs = processor(text=text, return_tensors="pt")
@@ -32,10 +59,14 @@ def synthesise(text):
     return speech.cpu()
 
 
+
+target_dtype = np.int16
+max_range = np.iinfo(target_dtype).max
+
 def speech_to_speech_translation(audio):
-    translated_text = translate(audio)
+    translated_text = translate(audio, transliterate)#
     synthesised_speech = synthesise(translated_text)
-    synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
+    synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
     return 16000, synthesised_speech
 
 
@@ -61,7 +92,7 @@ file_translate = gr.Interface(
     fn=speech_to_speech_translation,
     inputs=gr.Audio(source="upload", type="filepath"),
     outputs=gr.Audio(label="Generated Speech", type="numpy"),
-    examples=[["./example.wav"]],
+    #examples=[["./example.wav"]],
     title=title,
     description=description,
 )