add streaming usage

demo2.py

@@ -1,6 +1,9 @@
+import numpy as np
+import soundfile as sf
 import torch
 
 from model import FunASRNano
+from tools.utils import load_audio
 
 
 def main():
@@ -13,13 +16,26 @@ def main():
         else "cpu"
     )
     m, kwargs = FunASRNano.from_pretrained(model=model_dir, device=device)
+    tokenizer = kwargs.get("tokenizer", None)
     m.eval()
 
     wav_path = f"{kwargs['model_path']}/example/zh.mp3"
     res = m.inference(data_in=[wav_path], **kwargs)
-    text = res[0][0]["text"]
+    text = res[0][0]
     print(text)
 
+    chunk_size = 0.72
+    duration = sf.info(wav_path).duration
+    cum_durations = np.arange(chunk_size, duration + chunk_size, chunk_size)
+    prev_text = ""
+    for idx, cum_duration in enumerate(cum_durations):
+        audio, rate = load_audio(wav_path, 16000, duration=round(cum_duration, 3))
+        prev_text = m.inference([torch.tensor(audio)], prev_text=prev_text, **kwargs)[0][0]["text"]
+        if idx != len(cum_durations) - 1:
+            prev_text = tokenizer.decode(tokenizer.encode(prev_text)[:-5]).replace("<EFBFBD>", "")
+        if prev_text:
+            print(prev_text)
+
 
 if __name__ == "__main__":
     main()

model.py

@@ -354,6 +354,8 @@ class FunASRNano(nn.Module):
                     source_input = f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
             if not do_think:
                 source_input += "<think>\n\n</think>\n\n"
+            if kwargs.get("prev_text", None) is not None:
+                source_input += kwargs["prev_text"]
 
             splits = pattern.split(source_input)
             source_ids = []
@@ -514,7 +516,7 @@ class FunASRNano(nn.Module):
         fbank_beg = batch["fbank_beg"]
         fake_token_len = batch["fake_token_len"]
 
-        if not kwargs.get("tearchforing", False):
+        if not kwargs.get("teacherforcing", False):
             input_ids = source_ids
 
         input_ids[input_ids < 0] = 0
@@ -698,6 +700,7 @@ class FunASRNano(nn.Module):
                     skip_special_tokens=kwargs.get("skip_special_tokens", True),
                 )[0]
                 loss = model_outputs.loss.item()
+        response = kwargs.get("prev_text", "") + response
 
         ibest_writer = None
         if kwargs.get("output_dir") is not None: