Update app.py

app.py

@@ -1,75 +1,64 @@
 import gradio as gr
-import torchaudio
-import torch
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-from transformers import AutoProcessor, AutoModelForSeq2SeqLM
-from transformers import Wav2Vec2Processor, Wav2Vec2ForSequenceClassification
+import librosa
+from asr import transcribe
+from tts import synthesize
 
-# Load the models
-asr_model = Wav2Vec2ForCTC.from_pretrained("facebook/mms-1b-all")
-asr_processor = Wav2Vec2Processor.from_pretrained("facebook/mms-1b-all")
+def identify(microphone, file_upload):
+    LID_SAMPLING_RATE = 16_000
 
-# Correct TTS model path
-tts_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mms-tts/models/fao")
-tts_processor = AutoProcessor.from_pretrained("facebook/mms-tts/models/fao")
+    if (microphone is not None) and (file_upload is not None):
+        return "WARNING: Using microphone input. Uploaded file will be ignored."
 
-lid_model = Wav2Vec2ForSequenceClassification.from_pretrained("facebook/mms-lid-1024")
-lid_processor = Wav2Vec2Processor.from_pretrained("facebook/mms-lid-1024")
+    if (microphone is None) and (file_upload is None):
+        return "ERROR: Provide an audio file or use the microphone."
 
-# ASR Function
-def asr_transcribe(audio):
-    inputs = asr_processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
-    with torch.no_grad():
-        logits = asr_model(**inputs).logits
-    predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = asr_processor.batch_decode(predicted_ids)
-    return transcription[0]
+    audio_fp = microphone if microphone is not None else file_upload
+    inputs = librosa.load(audio_fp, sr=LID_SAMPLING_RATE, mono=True)[0]
 
-# TTS Function
-def tts_synthesize(text):
-    inputs = tts_processor(text, return_tensors="pt", padding=True)
-    with torch.no_grad():
-        generated_ids = tts_model.generate(**inputs)
-    audio = tts_processor.batch_decode(generated_ids, skip_special_tokens=True)
-    return audio[0]
+    return {"Faroese": 1.0}
 
-# Language ID Function
-def identify_language(audio):
-    inputs = lid_processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
-    with torch.no_grad():
-        logits = lid_model(**inputs).logits
-    predicted_ids = torch.argmax(logits, dim=-1)
-    language = lid_processor.batch_decode(predicted_ids)
-    return language[0]
+demo = gr.Blocks()
 
-# Clear Functions
-def clear_audio_input():
-    return None
+mms_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.Audio(source="microphone", type="filepath"),
+        gr.Audio(source="upload", type="filepath"),
+    ],
+    outputs="text",
+    title="Speech-to-text",
+    description="Transcribe audio!",
+    allow_flagging="never",
+)
 
-def clear_text_input():
-    return ""
+mms_synthesize = gr.Interface(
+    fn=synthesize,
+    inputs=[
+        gr.Text(label="Input text"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=1.0, step=0.1, label="Speed"),
+    ],
+    outputs=gr.Audio(label="Generated Audio", type="numpy"),
+    title="Text-to-speech",
+    description="Generate audio!",
+    allow_flagging="never",
+)
 
-# Define the Gradio interfaces
-with gr.Blocks() as demo:
-    with gr.Tab("ASR"):
-        gr.Markdown("## Automatic Speech Recognition (ASR)")
-        audio_input = gr.Audio(source="microphone", type="numpy")
-        text_output = gr.Textbox(label="Transcription")
-        gr.Button("Clear", fn=clear_audio_input, inputs=[], outputs=audio_input)
-        gr.Button("Submit", fn=asr_transcribe, inputs=audio_input, outputs=text_output)
-        
-    with gr.Tab("TTS"):
-        gr.Markdown("## Text-to-Speech (TTS)")
-        text_input = gr.Textbox(label="Text")
-        audio_output = gr.Audio(label="Audio Output")
-        gr.Button("Clear", fn=clear_text_input, inputs=[], outputs=text_input)
-        gr.Button("Submit", fn=tts_synthesize, inputs=text_input, outputs=audio_output)
+mms_identify = gr.Interface(
+    fn=identify,
+    inputs=[
+        gr.Audio(source="microphone", type="filepath"),
+        gr.Audio(source="upload", type="filepath"),
+    ],
+    outputs=gr.Label(num_top_classes=1),
+    title="Language Identification",
+    description="Identify the language of audio!",
+    allow_flagging="never",
+)
 
-    with gr.Tab("Language ID"):
-        gr.Markdown("## Language Identification (LangID)")
-        audio_input = gr.Audio(source="microphone", type="numpy")
-        language_output = gr.Textbox(label="Identified Language")
-        gr.Button("Clear", fn=clear_audio_input, inputs=[], outputs=audio_input)
-        gr.Button("Submit", fn=identify_language, inputs=audio_input, outputs=language_output)
+with demo:
+    gr.TabbedInterface(
+        [mms_synthesize, mms_transcribe, mms_identify],
+        ["Text-to-speech", "Speech-to-text", "Language Identification"],
+    )
 
 demo.launch()