funasr1.0.4 emotion2vec finetuned

游雁

2024-01-30 cf7f9a06c8067033a8f113591f9f8d96a3fbc3dd

funasr1.0.4 emotion2vec finetuned

 examples/industrial_data_pretraining/emotion2vec/demo.py

@@ -5,8 +5,9 @@

from funasr import AutoModel

model = AutoModel(model="damo/emotion2vec_base", model_revision="v2.0.4")
# model="damo/emotion2vec_base"
model = AutoModel(model="iic/emotion2vec_base_finetuned", model_revision="v2.0.4")

wav_file = f"{model.model_path}/example/test.wav"
res = model.generate(wav_file, output_dir="./outputs", granularity="utterance")
res = model.generate(wav_file, output_dir="./outputs", granularity="utterance", extract_embedding=False)
print(res)

 examples/industrial_data_pretraining/emotion2vec/infer.sh

@@ -1,5 +1,6 @@

model="damo/emotion2vec_base"
#model="damo/emotion2vec_base"
model="iic/emotion2vec_base_finetuned"
model_revision="v2.0.4"

python funasr/bin/inference.py \
@@ -7,4 +8,5 @@
+model_revision=${model_revision} \
+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
+output_dir="./outputs/debug" \
+extract_embedding=False \
+device="cpu" \

 funasr/models/emotion2vec/model.py

@@ -93,7 +93,10 @@
        if cfg.get("layer_norm_first"):
            self.norm = make_layer_norm(cfg.get("embed_dim"))


        vocab_size = kwargs.get("vocab_size", -1)
        self.proj = None
        if vocab_size > 0:
            self.proj = torch.nn.Linear(cfg.get("embed_dim"), vocab_size)


    def forward(
@@ -204,6 +207,9 @@
        #     assert sr == 16e3, "Sample rate should be 16kHz, but got {}in file {}".format(sr, source_file)
        #     assert channel == 1, "Channel should be 1, but got {} in file {}".format(channel, source_file)
        granularity = kwargs.get("granularity", "utterance")
        extract_embedding = kwargs.get("extract_embedding", True)
        if self.proj is None:
            extract_embedding = True
        meta_data = {}
        # extract fbank feats
        time1 = time.perf_counter()
@@ -211,6 +217,8 @@
                                                        data_type=kwargs.get("data_type", "sound"), tokenizer=tokenizer)
        time2 = time.perf_counter()
        meta_data["load_data"] = f"{time2 - time1:0.3f}"
        meta_data["batch_data_time"] = len(audio_sample_list[0])/kwargs.get("fs", 16000)
        
        results = []
        output_dir = kwargs.get("output_dir")
        if output_dir:
@@ -222,15 +230,28 @@
            source = source.view(1, -1)

            feats = self.extract_features(source, padding_mask=None)
            x = feats['x']
            feats = feats['x'].squeeze(0).cpu().numpy()
            if granularity == 'frame':
                feats = feats
            elif granularity == 'utterance':
                feats = np.mean(feats, axis=0)
            
            result_i = {"key": key[i], "feats": feats}
            results.append(result_i)
            if output_dir:
            if output_dir and extract_embedding:
                np.save(os.path.join(output_dir, "{}.npy".format(key[i])), feats)
            
            labels = tokenizer.token_list if tokenizer is not None else []
            scores = []
            if self.proj:
                x = x.mean(dim=1)
                x = self.proj(x)
                x = torch.softmax(x, dim=-1)
                scores = x[0].tolist()

            result_i = {"key": key[i], "labels": labels, "scores": scores}
            if extract_embedding:
                result_i["feats"] = feats
            results.append(result_i)

            
        return results, meta_data