test202410141642

app.py

@@ -3,5 +3,32 @@ import gradio as gr
 def greet(name):
     return "Hello " + name + "!!"
 
+
+import torch
+from transformers import pipeline
+speech_recognizer = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
+
+from datasets import load_dataset, Audio
+
+dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
+
+dataset = dataset.cast_column("audio", Audio(sampling_rate=speech_recognizer.feature_extractor.sampling_rate))
+
+result = speech_recognizer(dataset[:4]["audio"])
+print([d["text"] for d in result])
+
+# ==
+
+model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+classifier("Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers.")
+
+
 demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()