Create app.py

app.py

@@ -1,73 +0,0 @@
-import gradio as gr
-from transformers import Wav2Vec2Processor
-from transformers import AutoModelForCTC
-from conversationalnlp.models.wav2vec2 import Wav2Vec2Predict
-from conversationalnlp.models.wav2vec2 import ModelLoader
-from conversationalnlp.utils import *
-import soundfile as sf
-import os
-
-"""
-run gradio with
->>python app.py
-"""
-
-audioheaderpath = os.path.join(
-    os.getcwd(), "temp")
-
-
-pretrained_model = "codenamewei/speech-to-text"
-
-processor = Wav2Vec2Processor.from_pretrained(
-    pretrained_model)
-
-model = AutoModelForCTC.from_pretrained(
-    pretrained_model)
-
-modelloader = ModelLoader(model, processor)
-
-predictor = Wav2Vec2Predict(modelloader)
-
-audiofileexamples = ["example1.flac", "example2.flac"]
-
-fileextension = ".wav"
-
-
-def greet(*args):
-    """
-    List[tuple, tuple]
-    mic: param[0] (int, np.array)
-    audiofile: param[1] (int, np.array)
-    """
-
-    dictinput = dict(mic=args[0], file=args[1])
-    audiofiles = []
-
-    for key, audioarray in dictinput.items():
-
-        if audioarray is not None:
-            # WORKAROUND: Save to file and reread to get the array shape needed for prediction
-
-            audioabspath = audioheaderpath + "_" + key + fileextension
-            print(f"Audio at path {audioabspath}")
-            sf.write(audioabspath,
-                     audioarray[1], audioarray[0])
-            audiofiles.append(audioabspath)
-
-    predictiontexts = predictor.predictfiles(audiofiles)
-
-    mictext = predictiontexts["predicted_text"][0] + "\n" + \
-        predictiontexts["corrected_text"][0] if dictinput['mic'] is not None else ""
-    filetext = predictiontexts["predicted_text"][-1] + "\n" + \
-        predictiontexts["corrected_text"][-1] if dictinput['file'] is not None else ""
-
-    return [mictext, filetext]
-
-
-demo = gr.Interface(fn=greet,
-                    inputs=["mic", "audio"],
-                    outputs=["text", "text"],
-                    title="Speech-to-Text",
-                    examples=[audiofileexamples])
-
-demo.launch()  # share=True)