update

app.py

@@ -1,104 +1,28 @@
-import logging
-import sys
 import gradio as gr
 from transformers import pipeline, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
 
-logging.basicConfig(
-    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-    datefmt="%m/%d/%Y %H:%M:%S",
-    handlers=[logging.StreamHandler(sys.stdout)],
-)
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
 
 
 LARGE_MODEL_BY_LANGUAGE = {
     "Korean": {"model_id": "kresnik/wav2vec2-large-xlsr-korean", "has_lm": True},
 }
 
+p=pipeline('kresnik/wav2vec2-large-xlsr-korean')
 
-# LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())
-
-# the container given by HF has 16GB of RAM, so we need to limit the number of models to load
-LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())
-CACHED_MODELS_BY_ID = {}
-
-
-def run(input_file, language, decoding_type, history, model_size="300M"):
-
-    logger.info(f"Running ASR {language}-{model_size}-{decoding_type} for {input_file}")
-
-    history = history or []
-
-    if model_size == "300M":
-        model = LARGE_MODEL_BY_LANGUAGE.get(language, None)
-    else:
-        model = XLARGE_MODEL_BY_LANGUAGE.get(language, None)
-
-    if model is None:
-        history.append({
-            "error_message": f"Model size {model_size} not found for {language} language :("
-        })
-    elif decoding_type == "LM" and not model["has_lm"]:
-        history.append({
-            "error_message": f"LM not available for {language} language :("
-        })
-    else:
-
-        # model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
-        model_instance = CACHED_MODELS_BY_ID.get(model["model_id"], None)
-        if model_instance is None:
-            model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
-            CACHED_MODELS_BY_ID[model["model_id"]] = model_instance
-
-        if decoding_type == "LM":
-            processor = Wav2Vec2ProcessorWithLM.from_pretrained(model["model_id"])
-            asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer, 
-                           feature_extractor=processor.feature_extractor, decoder=processor.decoder)
-        else:
-            processor = Wav2Vec2Processor.from_pretrained(model["model_id"])
-            asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer, 
-                           feature_extractor=processor.feature_extractor, decoder=None)
-
-        transcription = asr(input_file, chunk_length_s=5, stride_length_s=1)["text"]
-
-        logger.info(f"Transcription for {input_file}: {transcription}")
-
-        history.append({
-            "model_id": model["model_id"],
-            "language": language,
-            "model_size": model_size,
-            "decoding_type": decoding_type,
-            "transcription": transcription,
-            "error_message": None
-        })
-
-    html_output = "<div class='result'>"
-    for item in history:
-        if item["error_message"] is not None:
-            html_output += f"<div class='result_item result_item_error'>{item['error_message']}</div>"
-        else:
-            url_suffix = " + LM" if item["decoding_type"] == "LM" else ""
-            html_output += "<div class='result_item result_item_success'>"
-            html_output += f'<strong><a target="_blank" href="https://huggingface.co/{item["model_id"]}">{item["model_id"]}{url_suffix}</a></strong><br/><br/>'
-            html_output += f'{item["transcription"]}<br/>'
-            html_output += "</div>"
-    html_output += "</div>"
-
-    return html_output, history
-
+def transcribe(audio, state=""):
+    time.sleep(2)
+    text = p(audio)["text"]
+    state+= text+ " "
+    return state, state
 
 gr.Interface(
-    run,
+    fn=transcribe,
     inputs=[
         gr.inputs.Audio(source="microphone", type="filepath", label="Record something..."),
-        gr.inputs.Radio(label="Language", choices=LANGUAGES),
-        gr.inputs.Radio(label="Decoding type", choices=["greedy"]),
-        # gr.inputs.Radio(label="Model size", choices=["300M", "1B"]),
         "state"
     ],
     outputs=[
-        gr.outputs.HTML(label="Outputs"),
+	"textbox",
         "state"
     ],
     title="Automatic Speech Recognition",