Update app.py

app.py

@@ -0,0 +1,65 @@
+import os
+
+os.system("pip install transformers")
+os.system("pip install https://github.com/kpu/kenlm/archive/master.zip")
+os.system("pip install pyctcdecode")
+os.system("pip install gradio")
+os.system("pip install librosa")
+
+import gradio as gr
+import librosa
+import torch
+
+from transformers import Wav2Vec2CTCTokenizer
+from transformers import Wav2Vec2FeatureExtractor
+from transformers import Wav2Vec2Processor
+from transformers import Wav2Vec2ForCTC
+from transformers import Wav2Vec2ProcessorWithLM
+
+repo_name = "aiface/vietnamese_s2t"
+
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+processor = Wav2Vec2ProcessorWithLM.from_pretrained(repo_name, token="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")
+model = Wav2Vec2ForCTC.from_pretrained(repo_name, token="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").to(device)
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(repo_name, token="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")
+tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(repo_name, token="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")
+
+def process_audio_file(file):
+    data, sr = librosa.load(file, sr = 16000)
+    
+    return data
+
+def transcribe(file_mic, file_upload):
+    warn_output = ""
+    if (file_mic is not None) and (file_upload is not None):
+       warn_output = "WARNING: You've uploaded an audio file and used the microphone. The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
+       file = file_mic
+    elif (file_mic is None) and (file_upload is None):
+       return "ERROR: You have to either use the microphone or upload an audio file"
+    elif file_mic is not None:
+       file = file_mic
+    else:
+       file = file_upload
+
+    input_values = process_audio_file(file)
+    input_dict = processor(input_values, sampling_rate=16_000, return_tensors="pt", padding=True)
+    logits = model(input_dict.input_values.to(device)).logits
+
+    pred_ids = torch.argmax(logits, dim=-1)[0]
+    pres = processor.batch_decode(logits.to("cpu").detach().numpy()).text
+    
+    return warn_output + str(pres[0])
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type='filepath', optional=True),
+        gr.inputs.Audio(source="upload", type='filepath', optional=True),
+    ],
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="Speech to text MMS With Language Model",
+    description="Demo đơn giản speech to text",
+)
+iface.launch(share=True)