First commit

README.md

@@ -1,12 +1,16 @@
 ---
-title: Robust Asr It
-emoji: 🚀
-colorFrom: green
-colorTo: yellow
+title: Italian Robust ASR
+emoji: 🎤
+colorFrom: red
+colorTo: green
 sdk: gradio
 app_file: app.py
-pinned: false
+pinned: true
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+# Italian Robust ASR
+
+Demo app for testing the model trained during the robust-speech-challenge by 🤗 HuggingFace
+
+Forked by [jonatasgrosman/asr](https://huggingface.co/spaces/jonatasgrosman/asr)

app.py

@@ -0,0 +1,110 @@
+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)
+
+
+DICT_MODELS = {
+    "robust-300m": {"model_id": "dbdmg/wav2vec2-xls-r-300m-italian-robust", "has_lm": True},
+    "robust-1b": {"model_id": "dbdmg/wav2vec2-xls-r-1b-italian-robust", "has_lm": True},
+    "300m": {"model_id": "dbdmg/wav2vec2-xls-r-300m-italian", "has_lm": True},
+}
+
+
+# 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
+MODELS = sorted(DICT_MODELS.keys())
+CACHED_MODELS_BY_ID = {}
+
+def run(input_file, model_name, decoding_type, history):
+
+    logger.info(f"Running ASR {model_name}-{decoding_type} for {input_file}")
+
+    history = history or []
+
+    model = DICT_MODELS.get(model_name)
+
+    if model is None:
+        history.append({
+            "error_message": f"Model size {model_size} not found for {language} language :("
+        })
+    elif decoding_type == "Guided by Language Model" 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 == "Guided by Language Model":
+            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"],
+            "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 = " + Guided by Language Model" if item["decoding_type"] == "Guided by Language Model" 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
+
+
+gr.Interface(
+    run,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath", label="Record something..."),
+        gr.inputs.Radio(label="Model", choices=MODELS),
+        gr.inputs.Radio(label="Decoding type", choices=["Standard", "Guided by Language Model"]),
+        "state"
+    ],
+    outputs=[
+        gr.outputs.HTML(label="Outputs"),
+        "state"
+    ],
+    title="Italian Robust ASR",
+    description="",
+    css="""
+    .result {display:flex;flex-direction:column}
+    .result_item {padding:15px;margin-bottom:8px;border-radius:15px;width:100%}
+    .result_item_success {background-color:mediumaquamarine;color:white;align-self:start}
+    .result_item_error {background-color:#ff7070;color:white;align-self:start}
+    """,
+    allow_screenshot=False,
+    allow_flagging="never",
+    theme="huggingface"
+).launch(enable_queue=True)

packages.txt

@@ -0,0 +1 @@
+ffmpeg

requirements.txt

@@ -0,0 +1,4 @@
+transformers
+torch
+pyctcdecode
+pypi-kenlm